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					                                                 Prevention and control of Dust and sand storms in North East Asia
                                                                                                      RETA 6068




                              Phased Program to Establish a
                              Regional Monitoring and Early
                              Warning System for Dust and Sand
                              storms in North East Asia

                                          WORKING DRAFT
                                                    (Revised)




            PREVENTION AND CONTROL OF DUST AND SANDSTORMS IN
                                             NORTH EAST ASIA
                                                    RETA 6068
                                                                                       December, 2003
Disclaimer: The views expressed in this report are those of the consultants and are not necessarily those of ADB or
UNEP nor of the governments of the People’s Republic of China, the Republic of Mongolia, the Republic of Korea or
of Japan.




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                                        Consultant register
   I.        PRC
Name             Specialty             Affiliation              Contact details
Mr Du Ping       Policy & Strategic    Institute of Spatial     e-mail duping@mx.cei.gov.cn
                 Planning              Planning &                Tel. 86 10 63908903
                                       Regional Economy,
                                       Beijing
Mr Zhang         Systems               Institute of Earth       e-mail xiaoye_02@163.net
Xiaoye           Development           Environment,             Tel. 86-29-8324369
                 Specialist            Chinese Academy
                                       of Sciences Xi’an
Ms Jiao Meiyan   Forecasting &         National                 e-mail jiaomy@cma.gov.cn
                 Early warning         Meteorological           Tel.86 10 68406169
                                       Center, CMA,
                                       Beijing
Mr Quan Hao      Ground Monitoring     National Research        e-mail
                                       Center for Analyses      quanhao@public3.bta.net.cn
                                       and Measurement,         Tel.86 10 84634255
                                       SEPA, Beijing
Ms Shao Yun      Remote Sensing        Institute of Remote      e-mail
                                       Sensing                  yunshao@public.bta.net.cn
                                       Applications,            Tel.86 10 64876313
                                       Chinese Academy
                                       of Sciences, Beijing
                 Financial Analyst     Institute of Spatial     e-mail yangping@amr.gov.cn
Ms Yang Ping                           Planning &               Tel.86 10 63908818
                                       Regional economy,
                                       Beijing

Mongolia

Ms M.              Remote sensing        Ministry of Nature      e-mail
Bayasgalan                               & Environment,          osm_info@mongol.net
                                         Ulaanbaatar             Tel. 976 11 327982
Ms T. Bulgan       Ground Monitoring     Central Laboratory      e-mail clem@mongol.net
                                         for Environmental       Tel. 976 11 341818
                                         Monitoring
Ms T. Solongo      Financial Analyst     Ministry of Nature      e-mail solongo@easy.com
                                         & Environment,          Tel. 976 11 312269
                                         Ulaanbaatar
Mr Yadmaa          Systems               Suhkbaatar District,    e-mail
Gantumur           Development           Ulaanbaatar             gyadmaa@yahoo.com
                                                                 Tel. 976 9 9886474

International consultant
Mr V. Squires     Team Leader          PO Box 31 Magill         e-mail
                                       5072                     dryland@senet.com.au
                                       Adelaide, Australia      Tel 61 8 8333 1279
                                                                Mobile (China)
                                                                13652934637




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                        Prevention and control of Dust and sand storms in North East Asia
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              ABBREVIATIONS AND ACRONYMS

ADB      –      Asian Development Bank
GEF      –      Global Environment Facility
DSS      –      dust and sandstorm
UNEP     –      United Nations Environment Programme
ESCAP    –      United Nations Economic and Social Commission for Asia and Pacific
UNCCD    –      United Nations Convention to Combat Desertification
UNCCD    –      United Nations Convention to Combat Desertification
PRC      –      People's Republic of China
ROK      --     Republic of Korea
SEPA     --     State Environment Protection Administration (China)
CMA      --     China Meteorological Administration
SFA       -     State forest Administration (China)
CAS      --     Chinese Academy of Sciences
KMA      --     Korea Meteorological Administration
JMA      --     Japan Meteorological Administration
NAMHEM   --     National Meteorology, Hydrology & Environmental Monitoring Agency,
                Mongolia
CAA      --     Civil Aviation Authority
TA       –      Technical assistance
EW       --     Early Warning
FC       --     Forecasting
PM10     --     Particulate matter of < 10
TSP      --     Total suspended particles
Lidar    --     Light Detection & Ranging (a scientific instrument)
ADEC     --     Asian Dust Experiment on Climate Change Impact
GTS      --     Global Meteorological Telecommunication System
WMO      --     World Meteorological Organization
GMS      --     Geostationary Meteorological Satellite.
MODIS    --     Moderate Resolution Imaging Spectroradiometer
TOMS     --     Total Ozone Mapping Spectrometer
NOAA            Satellite of National Oceanic & Aeronautical Administration
AVHRR           USA
TM       --     Thematic mapper (satellite mounted)
GAW      --     Global Atmospheric Watch
CNY      –      yuan (unit of Chinese currency)




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 1                                        Table of contents [ADD]
 2   Executive summary………………………………………………...…………………...4
 3   PART 1. BACKGROUND ………………………………………………………………….8
 4   1. Preamble on the origin of the study…………………………………………………..……...8
 5   2. What are DSS and why are they important in north east Asia………………………….. ………8
 6   3. Principles for classification of DSS phenomena…………………………………………….
 7     3.1 Classification of DSS………………………………………………………………
 8   4. Scientific and technical dimension of DSS……………………………………………
 9     4.1 Important processes in DSS……………………………………………………….
10     4.2 Factors triggering DSS and having implications on the development of DSS……
11        4.2.1 Weather factors……………………………………………………………….
12        4.2.2 Factors related to soil surface conditions……………………………………
13   5. Scope of the Report………………………………………………………………..
14     5.1 Geographic area…………………………………………………………………
15     5.2 Time horizons ………………………………………………………………
16   PART II FORECASTING AND EARLY WARNING
17   6. Forecasting and Early Warning as a means to minimize DSS impacts………..
18   7. Current monitoring program in the partner countries…………………………
19     7.1 PRC…………………………………………………………………………
20     7.2 Korea…………………………………………………………………………..
21     7.3 Japan……………………………………………………………………….
22     7.4 Mongolia…………………………………………………………………………
23   8. Indicators for monitoring DSS: its outbreak and transport…………………………
24     8.1 Major monitoring indices and analysis technologies………………………….
25   9. Ground surface monitoring: its role and effectiveness……………………………
26     9.1 Remote sensing for real-time monitoring……………………………………
27   PART III RATIONALE AND JUSTIFICATION FOR NETWORK
28   10. Why is a regional network needed: Rationale and justification for network…….
29   11. Objectives of the program…………………………………………………….
30      11.1 Goal……………………………………………………………………….
31      11.2 Objectives of the program…………………………………………………….
32         11.2.1 Outputs…………………………………………………………………….
33      11.3 Operational aspects of the proposed network……………………………….
34   11.4 What is purpose of regional Network?…………………………………………..
35   11.5 Centralized versus decentralized regional structure………………………………
36      11.5.1 Centralized systems
37      11.5.2 Decentralized networks
38    11.6 Measures to improve FC and EW capacity through the regional network…
39   12.Criteria for data selection…………………………………………………..
40      12.1 DSS forecasting elements………………………………………………..
41      12.2 Data required for DSS forecasting…………………………………….
42        12.2.1 Information about weather observations and analysis………………….
43        12.2.2 Geographic information and surface monitoring information……..
44        12.2.3 Dust related monitoring information…………………………………..
45   13. Network monitoring: national and regional……………………………………
46       13.1 Criteria for selection of network monitoring stations…………………..
47       13.2 Need to classify network monitoring stations & specify relevant instrumentation..
48       13.3 Recommended network monitoring sites in the region…………………………
49       PART III OPERATIONAL MECHANISM
50   14. Operational mechanism for data sharing for simulation modeling……………………..
51   15. Mechanism to share the simulation results among the partner countries……………….
52   16. Long-term endeavor and development planning
53      16.1 Recommendations on an urgent upgrade ……………………………………………… 66
54      16.2 An Action Plan for implementing a regional DSS Network in NE Asia………………. 69
55      16.3 A phased programme to develop a regional DSS monitoring and Early
56          warning network…………………………………………………………………………



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 1   17. Operational structure……………………………………………………………………..
 2   18. Cost estimates and financing plan………………………………………………………
 3           18.1 Budget estimates and costing for Mongolia………………………………………….
 4                   Budget estimates and costing for PRC
 5                   Budget estimates on on proposed regional DDS network……………………………….                71
 6   References……………………………………………………………………………………………
 7
 8   ANNEX
 9   Annex 1      LOGFRAME FOR RETA 6068
10   Annex 2      DSS FORECASTING AND EARLY WARNING: SIMULATION &
11                 MODELING……………………………………………………………………………….
12   1. DSS forecasting and early warning, simulation and modeling………………………….
13     1.1 DSS forecasting techniques based on weather forecasting……………………………..
14     1.2 Numerical DSS prediction techniques……………………………………………….
15     1.3 DSS early warning and advisory service………………………………………………..
16     1.4 Data and software requirements to implement the monitoring and early warning……
17   2. DSS simulation and modeling…………………………………………………………..
18     2.1 Proposed monitoring networks for DSS……………………………………………..
19     2.2 Inter-system data processing……………………………………………………….
20     2.3 Cross country data sharing and coordination………………………………………..
21
22




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 1   EXECUTIVE SUMMARY
 2
 3   Dust and sandstorm (DSS) is the generic term for a serious environmental phenomenon in Northeast Asia.
 4   It causes considerable hardship, loss of income, disrupts communications, affects peoples’ health and, in
 5   extreme cases, leads to death of people and their livestock and crops over large areas in the affected
 6   countries (P R China, Mongolia, Japan and the Korean peninsular countries RoK and DPRK).
 7
 8   To solve the serious long-range transboundary environmental problem of DSS, a regional cooperation
 9   mechanism must be established among the countries in the region so that the problem can be addressed in
10   a coordinated manner.
11
12   Considering the complex nature of the issue (scientific, political, socio-economic and the spatial and
13   temporal components) nothing less than a regional development program for establishing monitoring and
14   early warning network for DSS will do. It should be sought as the top priority. The benefits of such a
15   network will flow to the partner countries. The merit of cooperating is that it will be possible to achieve
16   much more through a network than by each country acting alone. Trans-boundary problems can most
17   effectively be solved through cooperation. There is considerable value-adding when neighbors combine
18   their efforts to combat DSS. Early warning of impending DSS events will be facilitated by data sharing
19   and rapid communications on the progress and geographic extent of any DSS outbreak.
20
21   To further improve regional cooperation in prevention and control of DSS there is a clear need to improve
22   cooperation and data sharing. Recognition of this has led to the development of phased program of
23   measures that involve actions by all four partner-countries over the next 5-7 years (and beyond).
24   Upgrading of monitoring sites, the strengthening of bilateral links for data sharing, exchange of scientific
25   knowledge and building on human resources and institutional capacity will be a high priority. It is obvious
26   that if the countries that receive the dust want to improve their data flow and enhance their forecasting
27   capacity. They may need to contribute to the upgrading and, in some cases, establishment of monitoring
28   sites in Mongolia and northern PRC. Good quality, reliable long-term monitoring will also be an essential
29   part of any mitigation program.
30
31   Whilst this report focuses on monitoring, forecasting and early warning it is important to bear in mind that
32   these are just part of the regional effort to prevent and control DSS.
33
34   Because of advances in technology and the development of new and better knowledge about the
35   mechanisms and conditions promoting DSS, it is planned to restrict the recommendations about
36   monitoring and early warning to the next 7-10 years.
37
38   It seems more achievable in the short term to encourage closer integration of the monitoring already in
39   place in each national system. Getting integration and data sharing nationally would represent a big
40   advance on the fragmented and often disparate systems in place in some counties (notably PRC).
41
42   Considering that there are four partner countries and a large number of monitoring stations, the monitoring
43   procedure for the DSS- NE Asia network has to be easy-to-use, have low operation costs and able to
44   provide quantitative real time data.
45
46   Ideally, a mechanism that provides a coordinated and effective mechanism to collect, analyze and
47   disseminate information on DSS and provide early warning (EW) of impending DSS events should be
48   established in NE Asia. The benefits of this mechanism are many and these are elaborated in this report.
49   The effectiveness of EW depends on the establishment and linking of monitoring stations in the partner
50   countries within the region and on the rapid transfer of data to the forecasters and modelers. It is because
51   of this that the present focus is on forecasting and EW. Forecasting of DSS involves several aspects: (i)
52   prediction of DSS onset (outbreak); (ii) prediction of transport routes and time of arrival at downstream
53   sites. It depends to a large extent on the meteorological data routinely collected by the national
54   meteorological administrations but can be refined by collecting additional data that is specific to DSS.
55




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 1   Early warning is valuable to several end users: policy makers and to industry and commerce; and to the
 2   general public as part of a national advisory or hazard warning system.
 3
 4   Both forecasting and EW rely on the availability of quality data that is rapidly transmitted to those who
 5   need it. Regional cooperation to facilitate data sharing will be a future development and the options for
 6   this are detailed here.
 7
 8   The mission of the proposed Regional DSS network
 9   The mission of the network is to provide a coordinated and effective mechanism to collect, analyze and
10   disseminate information on DSS and provide early warning (EW) of impending DSS events.
11
12   A specific regional DSS network would facilitate the free exchange of selected data sets that would assist
13   all partner countries (and those in North and central Asia generally) in mitigating the effects of DSS
14   events. Better forecasting and early warning helps industry and commerce, reduces the hazard and allows
15   the population to take preventative measures to safeguard property and human health.
16
17   The proposed regional NE Asian DSS Network would do several things (i) it would cause national
18   governments in each of the partner countries to review and evaluate their current monitoring efforts.
19   Hopefully too, it would lead to closer integration and cooperation among the various institutions and
20   agencies and lead to better data flow and standardization of data collection, handling and processing
21   procedures; (ii) Gaps in the present regional monitoring facilities will be identified and the opportunity to
22   prioritize the setting up of new or better facilities can be done in the context of how it might benefit all
23   regional partners; (iii) private sector support and funding for system establishment and on-going
24   maintenance is likely to be enhanced if an integrated and operational network is in place that delivers
25   more reliable and timely forecasts and warnings and which later yields tangible results in controlling and
26   preventing DSS; (iv) Data sharing and closer cooperation between neighbors would allow technology
27   transfer to occur faster and more efficiently. Software (e.g. prediction models) and hardware
28   developments would be available more readily to partners and advances in technology disseminated more
29   quickly.
30
31   Structure of the Data Collection System
32   Two issues must be considered when designing the system. Firstly, the collection of data from the DSS
33   monitoring stations. Secondly, the transmission of data among countries within the region.
34   Based on the above-mentioned options, the proposed data collection network is structured as an intra-
35   regional data collection and processing center at regional and national levels.
36
37   The responsibilities of these two levels are as follows:
38   The Regional Data Center
39       To collect the DSS observation.
40       To collect the weather observations based on GTS.
41       To collect other information such as surface observation, forecast and warning information
42       To integrate and disseminate the regional DSS information to national data centers.
43       To deliver the forecasting and early warning information to the national centers
44   The regional data center should be combined with the regional meteorological data center so that
45   it is easy to get global weather data related to DSS forecast
46
47    The National Data Center
48      To collect observation data from domestic DSS monitoring network that includes existing
49         network such as satellite observations, visibilities etc and the future network in the project.
50      To sent the data to regional center based on the DTS (Figure 10).
51      To disseminate data to domestic users for DSS forecasting and early warning.
52
53   The content of the data collected includes three sorts:
54       Basic meteorological data exchanged by various countries through GTS.
55       Data from the DSS monitoring stations under this project.


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 1          DSS forecasting and early warning information of various countries.
 2
 3   The proposed NE Asia-DSS network represents a coordinated way to mitigate the impacts of DSS on the
 4   atmosphere, on agriculture, forestry, and industry. In addition, it provides potent evidence to support
 5   efforts to accelerate measures to prevent and control DSS.
 6
 7   One of the important obligations of the Regional DSS network and its host institution(s) is to coordinate
 8   among the partners network-building efforts, and provide specific technological assistance and guidance.
 9   Programs will be designed for promoting the role of science and technology in preventing and controlling
10   DSS on the one hand and blending indigenous knowledge and modern science and technology on the
11   other, especially in the early warning system.
12
13   The launching of the proposed Regional DSS Network would provide opportunities for members of the
14   international community to put in concrete terms scientific cooperation against DSS in northeast Asia. In
15   particular, interested affected and developed country parties will be able to work more closely and
16   effectively, within the framework of the regional network, with international regional and sub-regional
17   organizations. All the partner countries are part of the WMO network. Opportunities exist to further
18   enhance these linkages and extend them to cooperation in the Asia-Pacific region (including USA,
19   Australia) and to Central Asia.
20
21   Institutional adjustments are required
22   In order to establish a regional DSS network there are a number of institutional adjustments required at
23   national and regional levels. The basic question here is What is the optimal organizational structure of a
24   network on regional monitoring and early warning on prevention and control of dust and sandstorms in
25   Northeast Asia?
26
27   This multilateral regional technical assistance project is very important but is full of great challenge to all
28   participating parties. There are at least two difficulties that we have to cope with.
29
30   Firstly, how to deal with the gaps in our present knowledge and foster cooperation between partner
31   countries e.g. PRC needs Mongolia’s data in real-time while Japan and Korea side need PRC real-time
32   data. But under the existing arrangements there are gaps and we cannot efficiently implement the long-
33   distance transport and forecasting models.
34
35   Secondly, how to deal with the investment-compensation in a fair and equitable way so that each country
36   that invested capital in setting up the monitoring sites can get recompense. Unless this problem is
37   addressed there may be a little positive activities and interest in better regional cooperation.
38
39   As for the organizational structure the main question is: who will play an essential and key role in the
40   regional network - an international organization or a country or a joint executive-agency which can be in
41   charge of coordinating interesting-parties involved and raising additional funds to promote each
42   country’s communication-system and maintain ongoing-costs of the regional network?
43
44   Two steps are necessary to establish a regional network. A decentralized regional organizational structure
45   should be set up in each partner country in advance of any attempt to get full integration at the NE Asian
46   regional level
47
48   In this step our main tasks are on: a) launch, as soon as possible, a study in each country to conduct a
49   comprehensive assessment of current networks that belong to different departments or institutions;
50   b) set up a joint planning group in each country empowered to develop a monitoring-strategy for DSS
51   (including agreement on technical standards), a draft a cooperation-framework.
52   c) agree on an acceptable compensation-mechanism whereby the real-time-data will be exchanged
53   between different interested parties. Only then will we go forward to the next step – a formal
54   organizational structure of the NE Asian regional network.
55




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 1   Without question we need to adjust institutional system or framework at both the national and regional
 2   levels. Naturally, the national level is fundamental and work should begin there on the three steps listed
 3   above. Meanwhile we have to strengthen existing bilateral or multilateral links.
 4
 5   Of the many elements that have bearing on the success of the network, proper design, involvement of the
 6   various stakeholders and employment of cost-affordable technologies are perhaps, the most essential. To
 7   be effective the Network will not be able to function without a sustained level of enthusiasm on the part of
 8   the participating members and commitment from their national governments
 9
10   Funding issues
11   The long term and continental-scale nature of the DSS problem raises questions about how such
12   undertakings should be financed and organized to ensure efficiency and continuity. A single year program
13   would be useless.
14
15   Resources must be gathered at a scale required for smooth and effective implementation of the various
16   activities and program area of the network. Whist it is absolutely essential that the member countries
17   make available funds from their own resources to the extent possible, it is also clear that external funding
18   will have to be mobilized for undertaking the various activities of the network.
19
20   Investments in appropriate technologies, particularly for electronic information exchange and electronic
21   transfer of information will be crucial to the successful operation of the DSS Network. It is likely that the
22   4 governments of the member countries will cover the cost of network coordination staff and other
23   personnel, and contribute to the operating expenses and a certain proportion of the required equipment
24   outlay. However, a considerable proportion of the network budget will have to be sourced externally and
25   this is where donor countries and agencies will play a key role in providing financial assistance. It should
26   be noted that the budget estimate did not take into account the possible share of the operating cost to be
27   met by the participating member countries either as in-kind contribution or inputted costs if the activities
28   are spearheaded or undertaken in their respective countries.
29
30   To be able to identify the weak points in the delivery of services, the regional network (however
31   configured) should be viewed as a process of data and information; flow through data capture, acquisition,
32   processing, storage, and packaging. These are then disseminated to end–users, policy decision makers,
33   private sector and the general public (in the case of impending hazards such as severe DSS events). All
34   the data, information, institutional arrangements, human resources and technology) must be integrated to
35   facilitate an efficient flow of information. This applies at all levels of operation (local, national, regional).
36   It is inevitable that upgrading of equipment will be involved.
37
38    A realistic timetable for implementation
39   Some things could be done within 12 months. Others will take much longer. This will depend on raising
40   the funds required, either through the various national government’s budgeting processes or through the
41   raising of external funds. There are also constraints imposed by the need to proceed in an orderly fashion
42   so that the upgrading and equipping of the monitoring stations can be in step. Data acquisition, data
43   transmission, data processing, storage and retrieval, and dissemination have to be developed in ways that
44   give maximum benefit and cost effectiveness.
45
46   Speedy operationalization and quality performance of the network will depend on the level of skills the
47   national coordinators possess and the efficacy of the communications between the national coordinators
48   and the members, partners, and other stakeholders and the regional support structures including UNEP,
49   ESCAP and others. The operationalization of the network would also depend on the commitment of the
50   various country parties on the formulation of well-focused program of work. A proposed Action Plan is
51   set out in Part V of this Report. Some actions have a suggested time-frame, others are on-going. Some
52   require considerable re-organization, others would be relatively simple to implement.
53
54   Key elements of a program to implement the regional DDS network




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 1   a) Developing the framework for the conduct of assessment and monitoring of DSS related events
 2   (including EW) at regional, sub-regional and national levels using in combination the various systems of
 3   information technologies and space-based technologies;
 4   b) Supporting a national focal point to enhance and improve the linkage of national databases with
 5   regional and sub-regional databases applying digital and communication technology;
 6   c) Developing a regional framework for the conduct of joint or collaborative information gathering and
 7   database consolidation for scientific information on DSS related matters, including desertification control
 8   d) Formulating programs that will provide for analysis and interpretation of data into usable form
 9   e) Encourage the use of information generated by the network and devise systems for the transfer of this
10   information to decision makers, and relevant end users (including citizens of affected areas)
11   f) Develop training and research programs to improve capacity building efforts at the national level.
12
13   In Phase 1, the network of stations will compose 25 in PRC and 5 Mongolia plus stations in Korea and
14   Japan if needed, which could be part of a chain of monitoring station from the source areas to the
15   depositional areas. Apart from standard meteorological data, the main indicator would be VISIBILITY. A
16   target in the near-term would be to upgrade each site to an instrumented one (to do away with the
17   subjectivity associated with visual estimates).
18
19   The phased development should aim at upgrading selected monitoring sites to full capability in all of these
20   over the near- to medium-term. Selected stations might warrant Lidar installations. The selected stations
21   along the chain of stations (including those in the DSS source areas) would enjoy a high priority, at least.
22
23   Phase 2 could include adding additional monitoring stations to the network (up to about 40 in total).
24   Mongolia would have a chance to build up its stations as part of the longer-term development under the
25   regional Master plan. The main focus in Phase 2 is to add PM10 monitoring capability to each site in the
26   network. Because this is available in real-time and it is more useful than TSP, although technological
27   advances in high-speed sampling and analysis may make TSP more useful in future for forecasting and
28   EW.
29
30   Phase 2 would also be a time to improve Mongolian capacity in modeling and simulation, training of
31   personnel for monitoring stations, data processing and interpretation etc. The possibility of getting
32   external funding for this aspect should be explored.
33
34   Phase 3 the further development of sites to include Lidar would be a feature of this phase. Selected
35   stations along the chain of stations (including those in the DSS source areas) would be a high priority.
36
37   Setting up a regional data bank (perhaps involving Russia, DPRK (North Korea), Kazakstan etc) should
38   also be considered as part of this phase. Its functions could include training, information exchange on
39   appropriate technologies etc. The overall objective would have the 4 partner countries reach similar a level
40   in terms of national capacity to monitor DSS, forecast outbreaks (onset) and predict transport routes,
41   transit times and likely duration and geographic extent.
42
43   Recommendations
44   Recommendation 1 The Data Collection System for DSS forecasting and early warning should be
45   established on the existing and operational Meteorological Information System. This cost-effective
46   approach enables the DSS monitoring network to operate once commissioned, the performance being seen
47   immediately. In most of countries of the world, the government mandates that all the forecasting and EW
48   responsibilities to meteorological agencies. If the Meteorological Administrations of each country are not
49   involved in the regional network, it would be hard to achieve one the major goal of this program,
50   forecasting and EW of the DSS.
51   Recommendation 2. All observation-sites, no matter which institution\agency owns them, should collect
52   common data or information in accordance with an agreed indicator-system and technological standard.
53   That means that data from hundreds of sites in PRC, Mongolia, Republic of Korea and Japan would form
54   the basis for an effective monitoring and early warning system. This would create significant cost saving.
55   Funds could become available to construct new observation-sites or upgrade existing ones (if required) or



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 1   to build institutional capacity, buy modern instruments and software, develop human resources, promote
 2   communication systems, maintain data-banks etc.
 3   Recommendation 3. The Data Collection System for DSS forecasting and early warning should be
 4   established on the existing and operational Meteorological Information System. This cost-effective
 5   approach enables the DSS monitoring network to operate once commissioned, the performance being seen
 6   immediately.
 7   Recommendation 4. All partner countries should strive to provide timely and early warning and advisories,
 8   as appropriate, to affected citizens through the various media channels so that DSS effects will be
 9   mitigated.
10   Recommendation 5. Initiate a major large-scale dust emission inventory to identify dust sources and
11   provide quantifiable estimates of potential dust entrainment.
12   Recommendation 6. Efforts be mounted to seek funding, including emphasis on cost recovery options, the
13   setting up of a Trust or Foundation to attract financial inputs from the private sector, bilateral and
14   multilateral donors and NGOs
15
16




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 1   PART I BACKGROUND
 2
 3   1. Preamble on origin of the study
 4
 5   Dust and sandstorm (DSS) is the generic term for a serious environmental phenomenon in Northeast Asia.
 6   It causes considerable hardship, loss of income, disrupts communications, affects peoples’ health and, in
 7   extreme cases, leads to death of people and their livestock and crops over large areas in the affected
 8   countries.
 9
10   At the request of the governments of the PRC and Mongolia, in early May 2002 the Asian Development
11   Bank (ADB) started to prepare a regional technical assistance (TA) for prevention and control of DSS in
12   Northeast Asia. In parallel, three agencies of the United Nations—United Nations Environment Program
13   (UNEP), United Nations Economic and Social Commission for Asia and Pacific (ESCAP), and United
14   Nations Convention to Combat Desertification Secretariat (UNCCD)—initiated a similar project proposal
15   to seek support from the Global Environment Facility (GEF) to address the same environmental problem
16   in the region.
17
18   To integrate the support from the international community and maximize its effects, at a meeting among
19   the environment ministries of PRC, Japan, Republic of Korea, and Mongolia in June 2002, the
20   governments of the four countries proposed that ADB, ESCAP, UNCCD, and UNEP jointly develop an
21   expanded and integrated TA to promote regional cooperation on DSS to be co-financed by GEF. A joint
22   fact-finding and consultation mission comprising representatives from ADB, ESCAP, UNCCD, and
23   UNEP and led by ADB visited PRC and Mongolia from 26 August to 2 September 2002. The mission
24   reached an understanding with the governments of PRC and Mongolia on all aspects of the TA including
25   the goals, purpose, scope, implementation arrangement, cost estimates, financing plan, and terms of
26   reference.
27
28   ADB is the Executing Agency, responsible for overall management and administration of the TA. The TA
29   has been implemented in cooperation with ESCAP, UNEP and UNCCD. UNEP, which serves as the GEF
30   implementing agency for the GEF co-financing resources, chairs the technical committee for developing a
31   program for establishing a regional monitoring and early warning network for DSS and comprehensively
32   assessing the scientific findings.
33
34   2. What are DSS and why are they important in Northeast Asia?
35
36       (i) DSS involves strong winds that blow a large quantity of dust and fine sand particles away from
37              the ground and carry them over a long distance with severe environmental impacts along the
38              way, and often with severe impacts across the countries in the region. The major sources of
39              DSS in the region are the desert and semi-desert areas of the People’s Republic of China
40              (PRC) and Mongolia (DSS originating source areas). Long-range transport of dust aerosol
41              particles links the biogeochemical cycles of land, atmosphere and ocean, possibly even
42              influencing the global carbon cycle, and having a significant effects on regional radiative
43              balances, and human health. A geochemically significant quantity of dust from Asian sources
44              regions, estimated to be 400-500 Tg, is deposited in the North Pacific each year.
45              Approximately 240 Tg of dust is re-deposited in Chinese deserts each year, 140 Tg falls out
46              over other parts of China.
47       (ii)   DSS as a natural phenomenon has occurred for thousands of years in the region. During the
48              past 50 years, however, the frequency has increased, geographic coverage has expanded, and
49              damage intensity has accelerated. PRC statistics available indicate that average occurrence of
50              DSS was 5 times a year in 1950s, 8 times in 1960s, 14 times in 1970s, and 23 times in 1990s.
51              The region experienced 32 DSS in 2001, and the most severe DSS for decades in early 2002.
52       (i)    Large-scale DSS has significant environment effects that cause enormous economic losses,
53              present serious public health concerns over a wide geographic area, and, sometimes take
54              human life. For instance, the DSS on 5 May 1993 directly affected 1.1 million square
55              kilometers in the PRC, which resulted in 85 dead, 246 injured, 4,412 houses destroyed,



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 1                 120,000 livestock dead or lost, and 373,000 hectares of crop land damaged1. The direct
 2                 economic cost of this DSS within the PRC alone was more than CNY550 million (about $66
 3                 million at the current exchange rate). The two most severe DSSs in decades took place in
 4                 March and April 2002. They swept across Mongolia and hit 18 provinces in PRC, the Korean
 5                 Peninsular, and a large area of Japan. Total suspended particulate levels in these affected areas
 6                 were recorded from tens to hundreds of times higher than the national standards in these
 7                 countries. The DSS in early April was so severe that Mongolia had to close its international
 8                 airport in Ulaanbaatar for 3 days, and the Republic of Korea had to close primary schools and
 9                 cancel more than 40 flights departing from Kimpo Airport in Seoul. Satellite images of DSSs
10                 and analysis of the dust samples collected on the ground have revealed that impacts of strong
11                 DSSs are not limited to the region, but reach as far as North America across the Pacific
12                 Ocean.
13        (ii)     A preliminary study indicates that 18 of the 32 DSS events in 2001 originated from the deserts
14                 of Mongolia, and the remaining 14 originated from the desert or semi-desert areas of Inner
15                 Mongolia Autonomous Region, in PRC. Natural elements, large desert and semi-desert areas,
16                 the strong winds from Siberia sweeping these DSS originating source areas, severe and
17                 persistent drought, late killing frosts and other natural disasters, contribute to DSS. Their
18                 effects have been strengthened and intensified significantly, however, by human interventions
19                 over the last few decades, particularly through overgrazing, overly-optimistic conversion of
20                 grassland to cropland, deforestation, and over-exploitation of water resources in the DSS
21                 originating source areas, which led to rapid land degradation and desertification. Although all
22                 the countries in the region are affected by DSS, effective actions are urgently needed in the
23                 DSS originating source areas in PRC and Mongolia to arrest deterioration of the land, before
24                 the situation becomes irreversible.
25        (iii)    DSS represent a long-range transboundary environmental problem that is now having quite
26                 serious impacts in northeast Asia. In the DSS-affected areas, hundreds of millions of people
27                 have been exposed to the impacts of DSS on living standards, public health, and economic
28                 wealth.
29   (iv)       In addition to various initiatives of the governments, non-government organizations and
30                 volunteers from the DSS-affected countries have been actively undertaking cross-border
31                 activities to mitigate DSS events (e.g., planting trees in the DSS originating source areas), but
32                 in a sporadic and uncoordinated manner.
33
34   3. Principles for classification of DSS phenomena
35
36   3.1 Classification of DSS
37   DSS is the general term for dust storm and sandstorm. It refers to the phenomenon in which dust and sand
38   are blown into the sky, seriously affecting horizontal visibility. The occurrence of DSS is built upon two
39   prerequisites. They are (i) dry and loose surface and a (ii) strong2 and persistent wind. In meteorology
40   DSS has long been treated as one of the observational elements, as it is classified as a disastrous weather
41   phenomenon. It has been further divided into 11 categories by the World Meteorological Organization
42   (WMO) to represent different characteristics and developing stages of the phenomenon (see Table 1).
43
44   WMO has not established unified criteria for distinguishing these categories. Therefore different criteria
45   are used in different countries. In its technical regulation for operational observation, China
46   Meteorological Administration has classified DSS into 4 categories in accordance with visibility and wind
47   speed (see Table 2). In the Republic of Korea, a dust and sand concentration observation network has been
48   established to measure PM10 value, which is used to determine the category of DSS
49
50
51

     1
       See also Yang Youlin and Lu Qi In “Global alarm: dust and sandstorms from the World’s Drylands‖. UN 2002 for
     an account of the severe DSS event in the Hexi corridor of Gansu Province, PRC
     2
       Generally 6.5 metres/second (m/s) is regarded as the threshold wind velocity to initiate a dust outbreak provided
     that the soil surface is dry



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1   Table 1. The classification of DSS by WMO
2                              (Meteorological codes associated with sand storm)
     Code     Symbol       Remarks (English)                         Remarks (Chinese)
                          Widespread dust in             非 测 形 的 面 浮
                                                          观 时 成 大 积 尘
                          suspension in the air not
       6                  raised by wind at time of
                          observation

                          Dust or sand raised by          力 扬 沙 ( 观 时 为 )
                                                         风 吹 的 尘 以 测 间 准
       7                  wind at time of
                          observation

                          Well developed dust            在 去 小 形 的 卷
                                                          过 一 时 成 尘
       8                  devil(s) within past hour


                          Dust storm or sand storm       在 去 小 内 以 测 的 尘
                                                          过 一 时 可 观 到 沙 暴
                          within sight of the station
       9                  during past hour


                          Slight or moderate dust        在 去 小 内 弱 轻 或 度 尘
                                                          过 一 时 减 的 度 中 沙 暴
                          storm or sand storm has
       30                 decreased during past
                          hour

                          Slight or moderate dust        在 去 小 内 明 变 的 度 中 沙 暴
                                                          过 一 时 无 显 化 轻 或 度 尘
                          storm or sand storm no
       31                 appreciable change during
                          past hour

                          Slight or moderate dust        在 去 小 内 强 轻 或 度 尘
                                                          过 一 时 增 的 度 中 沙 暴
                          storm or sand storm has
       32                 increased during past
                          hour

                          Severe dust storm or           在 去 小 内 弱 强 尘
                                                          过 一 时 减 的 沙 暴
                          sand storm has
       33                 decreased during past
                          hour
                          Slight or moderate dust        在 去 小 内 明 变 的 度 中 沙 暴
                                                          过 一 时 无 显 化 轻 或 度 尘
                          storm or sand storm no
       34
                          appreciable change
                          during past hour
                          Slight or moderate dust        在 去 小 内 强 轻 或 度 尘
                                                          过 一 时 增 的 度 中 沙 暴
                          storm or sand storm has
       35                 increased during past
                          hour
                          Thunderstorm                    测 有 暴 沙 暴
                                                         观 时 雷 和 尘
                          combined with
       98                 duststorm or sandstorm
                          at time of observation
3
4
5
6




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                                                   Prevention and control of Dust and sand storms in North East Asia
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 1   Table 2. The definition of DSS by China Meteorological Administration: an example
 2
     Categories               Characteristics                  Horizontal visibility     Weather condition
     Drifting dust            Dust suspending in the air.      <10 km                    Weak wind

 3
     Blowing dust             Dust or sand raising by wind     <10 km                    Moderate wind


     Dust and sand storm      Dust and sand raising by         <1 km                     Strong or turbulent
                              strong or turbulent wind                                   wind

     Severe dust and sand     Dust and sand raising by very    <50 m                     Very Strong or
     storm                    strong or turbulent wind                                   turbulent wind

 4
 5
 6   4. Scientific and technical dimension of DSS problem
 7
 8   4.1 Important processes in DSS
 9   An understanding of how and why DSS events occur involves study of the climatic factors and the
                                                            10     dynamics of the weather as well as the
                                                            11     land/atmosphere interface. DSS of
                                                            12     Northeast Asia, mainly originate from
                                                            13     Mid-latitude Desert Zone (N 40-45°E
                                                            14     90-120°). Driven by the East Asia
                                                            15     Winter Monsoon, DSS generated from
                                                            16     above areas moves to southeast then to
                                                            17     east along N 40° parallel, passing over
                                                            18     the Korea Peninsula and Japan to the
                                                            19     northern Pacific Ocean (Figure 1).
                                                                  20
                                                                  21   Normally, it takes one or two days to
                                                                  22   move from sources to Korea, two or
                       Figure 1 DSS transport processes           23   three days to Japan.
                                                                  24   In winter and spring, the area of N 40-
25   45° is under the influence Mongolian High pressure system which is cold and dry, while the land surface
26   is very cold. Seasonal soil freezing may occur but temperature differentials between the land surface and
27   the air mass develop as spring progresses.
28
29   4.2 Factors triggering DSS and having implications on the development of DSS
30   Occurrence of DSS depends on two factors: (1) surface wind speed and (2) soil surface properties.
31
32   4.2.1    Metrological condition:
33
34   The occurrence of DSS is associated with dry weather. Strong wind and unstable air-flow are the main
35   dynamic factors for triggering DSS, while the strong wind occurs normally under a typical atmospheric
36   circulation.
37             Cold wave – Large scale DSS is always associated with strong cold wave which comes from
38             Siberia and Mongolia areas in winter and spring generating strong air motion and thus providing
39             favorite dynamic conditions for the occurrence and development of DSS.
40             Cyclone weather system – Mongolian cyclone is a typical weather system that may trigger and
41             consequently facilitate the development of DSS. Strong Mongolian cyclone will form eddy
42             circulation. Its size can be from the surface to a height of several thousand meters within which
43             air flows violently both in the horizontal and vertical direction. Such a weather pattern is



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 1             favorable for the dust emission (outbreak) and transportation of DSS. Therefore eddy circulation
 2             is an inducement for DSS.
 3             Atmospheric thermo-instability – In desert areas, the near surface thermo-instability will cause
 4             vertical air motion and near surface turbulent motion, which will induce wind erosion and
 5             vertical transport of soil.
 6             The sharp change of weather elements – Before and after the occurrence of DSS, some
 7             meteorological elements will change significantly such as low level pressure, temperature,
 8             humidity, wind and visibility etc.
 9
10   As DSS occurs under some typical weather conditions, the evolution of DSS outbreaks is predicable.
11   Weather forecasts are the scientific basis for DSS forecasting (see below).
12
13   4.2.2. Soil surface properties
14   The most critical surface parameters controlling the soil dust emission and associated DSS are the surface
15   roughness length (it is highly related with land use/cover), soil texture and moisture content. There is very
16   limited information with respect to a soil grain size distribution in the DSS source areas of NE Asia.
17
18   Dry and loose soil surface is the prerequisite for DSS. The soil and vegetation category, and the coverage
19   of vegetation in the DSS source area are all the factors affecting the DSS outbreak (see below).
20
21   A useful conceptual model to help us better appreciate the factors and distances involved is presented
22   below. The researchers in the ADEC project developed this3. It recognizes three important processes in
23   each DSS event
24
25   a. The mechanism of the DSS ―outbreak‖ (contributing factors and ―drivers‖)
26   b. The mechanism of long-range transport (contributing factors and ―drivers‖)
27   c. The evaluation of DSS and its impact (on people, on commerce and on the regional climate via its
28      effect on radiative forcing).
29
30   The first steps are to measure the surface conditions, even the dust flux and the associated meteorological
31   conditions in the source regions. These involve long term monitoring at ground based monitoring stations.
32   Secondly, airborne measurements are also used to assess dust transport, usually via aerosol sampler,
33   weather balloons and instruments like transmissometer (visibility), sun photometer (optical depth), radar,
34   radiometers and lidar (light detection and ranging equipment). Dust collection through sampling
35   equipment installed along the expected storm path aids in the analysis and identification of dust sources.
36   Satellite data are also used as both PRC and Japan have dedicated weather satellites. Data can also be
37   obtained from geostationary and polar-orbit satellites maintained by USA, Russia and other nations.
38   Thirdly, analysis of long-term weather records, and associated data on DSS results in data sets for the
39   development of transport models as predictors of the DSS behavior and its impact on long-term climate
40   change through the effect of radiative forcing4.
41
42   Box 1 Lidar - a useful tool for DSS monitoring
43
44   Lidar (Light Detection and Ranging) is a kind of radar using light instead of radio waves. It transmits pulsed laser
45   light to the atmosphere: and collects the light reflected from the atmospheric molecules, clouds, and aerosols with the
46   help of a receiving microscope. Distribution of the atmospheric minor constituents of vapor, temperature, aerosols,
47   and clouds are derived from the intensity of the received light; and distribution of wind is derived from the Doppler
48   shift.




     3
       Aerosol Dust Experiment on Climate Change Impact. (ADEC) A cooperative effort between Japan and China to
     assess the mechanism of dust supply to the atmosphere on a global scale and to evaluate the impact of Aeolian dust
     on climate through radiative forcing.
     4
       Radiative forcing refers to the adsorption and scattering of both incoming and terrestrial radiation. It has the
     potential to impact on the global climate …



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                                                   Prevention and control of Dust and sand storms in North East Asia
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 1
 2   A Schematic of how Lidar works.
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26   Figure 2 A chart produced as part of the ADEC project that shows the major components of DSS events
27   that arise in western China. Of course winds from the north are also implicated in the transboundary
28   transport of dust aerosols to the Korean peninsular, to Japan and beyond.
29
30   5. Scope of this report
31
32   Four countries (PRC, Mongolia, Republic of Korea and Japan) are involved in the present project. These
33   countries already enjoy a series of bilateral arrangements to monitor and mitigate the effects (see below).
34   There is considerable opportunity to build on these efforts and develop an integrated regional approach to
35   DSS prevention and control.
36
37
38
39
40
41
42




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 1   5.1 Geographic area
                                                                        2   The geographic area covered by the
                                                                        3   project includes part of continental
                                                                        4   Asia (Mongolia, PRC and the Korean
                                                                        5   peninsular) and the neighboring
                                                                        6   islands of Japan. The wind and
                                                                        7   weather patterns that ―drive‖ the
                                                                        8   system may originate in the Russian
                                                                        9   Federation to the north and west and
                                                                       10   in Kazakstan to the west of Mongolia
                                                                       11   and PRC (see Figure 3) and the DSS
                                                                       12   impact may be felt in North Korea
                                                                       13   (DPRK) and in North America. Thus
                                                                       14   DSS is an example of a trans-
                                                                       15   boundary environmental problem.
                                                                       16   Perhaps the ultimate solution may rest
                                                                       17   with involving the neighboring
                                                                       18   countries that are outside the current
                                                                       19   partnership but for the present there is
                                                                       20   much that can be done through
        Figure 3 DSS affects the four partner countrie (PRC, Mongolia, 21   regional cooperation involving the
        Republic o f Korea and Japan) but involves neighbors such the 22    four partner countries in this RETA.
        far eastern regions of the Russian Federation, Kazakstan, and 23
        North Korea(DPRK).
                                                                       24
                                                                       25
26   As the outline of the project framework shows (Annex 1) the overall objective is to prevent and control
27   DSS in the NE Asia region. This report though covers only the part that deals with monitoring and early
28   warning. It presents a phased program to strengthen the monitoring capacity in the two source counties
29   (PRC and Mongolia) and to improve the information flow among all four partner-countries through the
30   development of an effective regional network.
31
32   5.2 Time horizons
33   To further improve regional cooperation in prevention and control of DSS there is a clear need to improve
34   cooperation and data sharing (see above). Recognition of this has led to the development of phased
35   program of measures that involve actions by all four partner-countries over the next 20 years (and
36   beyond). The regional action plan will have a time horizon from the near future (2004-5) extending to 20
37   years and beyond. Because of advances in technology and the development of new and better knowledge
38   about the mechanisms and conditions promoting DSS, it is planned to restrict the recommendations about
39   monitoring and early warning to the next 7-10 years.
40
41   Upgrading of monitoring sites, the strengthening of linkages for data sharing, exchange of scientific
42   knowledge and building on human resources and institutional capacity will be a high priority. It is obvious
43   that if the countries that receive the dust want to improve their data flow and enhance their forecasting
44   capacity. They may need to contribute to the upgrading and, in some cases, establishment of monitoring
45   sites in Mongolia and northern PRC. Good quality, reliable long-term monitoring will also be an essential
46   part of any mitigation program.
47
48   Whilst this report focuses on monitoring, forecasting and early warning it is important to bear in mind that
49   these are just part of the regional effort to prevent and control DSS.
50
51   5.3. Definitions and terminology
52
53   Some commonly used definitions for the terms monitoring, early warning, forecasting are shown below.
54
55




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 1   Box 2 Some commonly used definitions
 2
 3   Monitoring
 4
 5   Definition:                     Monitoring is the systematic and repeated observation of a specific phenomenon,
 6                                   usually organized through appropriate planning for some particular purposes .
 7   Objects under Monitoring
 8
 9   Target Events:                  Monitoring is focused on the occurrence frequency, significance or severity, dimension
10                                   and duration of the target events;
11
12   Contributing Factors:           Monitoring is also made on the known factors that may contribute to the occurrence,
13                                    severity, or dimension of the monitored events such as climatic conditions, and land
14                                   surface conditions in the case of DSS; and
15
16   Impacts of the Events:          Impacts of the events are also monitored such as damage to infrastructure, social and
17                                   economic activities, air quality, and public health in the case of DSS.
18   Objectives of Monitoring
19
20   Event Understanding:            Monitoring can help build up a profile of the monitored event for better understanding
21                                   of the event, which can help for preparing appropriate actions for or response to the event.
22
23   Analysis Facilitation:          For more complicated events, monitoring is needed to facilitate a more comprehensive
24                                   analysis of the monitored events, usually based on a large quantity of the monitored data
25                                   and through application of very sophisticated analysis instruments.
26   Forecasting
27
28   Definition:                     Forecasting is an assessment made, based on the analysis of the monitoring data, of the
29                                   possibility of the occurrence of certain event in a certain future timeframe, which may
30                                   include the scope, dimension, severity, duration, and impacts of the events under
31                                   consideration.
32
33   Classification:                 Forecasting can be classified by different criteria. One commonly used classification
34                                   is based on the timeframe of the forecasting assessment: i.e. short term forecasting for
35                                   the possibility of occurrence of the event within a short timeframe, say, of one or two
36                                   days; medium term forecasting for one or two weeks; or long term forecasting for one
37                                   or two years. The actual duration varies from place to place and depends on the purpose.
38   Early Warning
39
40   Definition:                     Early warning is the advice that is provided, normally based on monitoring and
41                                   forecasting analysis, for the concerned parties of a forthcoming event before it arrives.
42
43   Purpose of Early Warning:       The purpose of early warning is to improve the awareness and preparedness by the
44                                   concerned parties of the forthcoming event and its possible consequence.
45
46
47   The relationship among monitoring, forecasting, impact assesses, and early warning are shown in Figure 4
48   :
49      (a) DSS Forecasting System (FS) normally consists of three components: (1) Weather forecast
50           Model, (2) Dust Aerosol Module, and (3) Data Assimilation System
51      (b) Monitoring can provide absolute values of visibility, TSP, PM10 etc, mainly derived from
52           surface- based observation, and data of land use/cover, soil moisture as well as the spatial
53           distribution of DSS derived from satellite-based observation. All the information should be
54           quickly transferred to Data Assimilation System of DSS Forecasting system (FS) to provide new
55           dust initial condition for rolling forecasting. The soil moisture data retrieved from satellite can
56           also be compared with the model-estimated data derived from Dust Aerosol Module in the DSS
57           FS.




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 1       (c) Because the DSS FS can output the dust emission flux, the direct indicator of the source of DSS,
 2           source information can serve as the basis of DSS source identification for government decision
 3           makers.
 4       (d) The spatial distribution and deposition of dust aerosol derived from DSS FS can be employed as
 5           the scientific basis to assess the impact of DSS to human health, economic loss etc. It is also in
 6           line with the government expectations.
 7       (e) Early warning (EW) makes it possible to reduce the impact of DSS on human health, important
 8           commercial activities etc., which highly depend on the accurate forecasting hours before a DSS
 9           attacks a city, and the dust concentration (µg m-3) at different heights. The results from DSS FS
10           can be used in two ways: (1) just for exchanging information among different DSS FS center,
11           called ―Internal‖, and (2) for government to release the DSS EW to public, called ―Public‖
12           (Figure 4)

        Indicators:
        Atmosphere
        -Meteorological parameters
        -Ambient At mosphere (visibility, TSP, PM10 etc.)
        Ground surface
        -Land use/cover change
        -soil moisture

                                                                                      DSS Source
                                               DSS Forecasting System
                                                                                      identification
        Monitoring :                           - Weather fo recast Model
         Surface-based Observations            - Dust Aerosol Module
         Satellite-based Observations          - Data Assimilation System             Impact assessment
                                                                                      Health, econo mic loss


                                          Earl y warning
                                          - Internal: Country’s DSS FS Center
                                          - Public: Govern ment


      Figure 4 Relationships between indicators, monitoring, forecast ing and early warn ing for DSS in NE Asia
13
14   6. Why are Forecasting and Early Warning important as a means to minimize DSS impacts
15
16   Ideally, a mechanism that provides a coordinated and effective mechanism to collect, analyze and
17   disseminate information on DSS and provide early warning (EW) of impending DSS events should be
18   established in NE Asia. The benefits of this mechanism are many and these will be elaborated in this
19   report. The effectiveness of EW depends on the establishment and linking of monitoring stations in the
20   partner countries within the region and on the rapid transfer of data to the forecasters and modelers. It is
21   because of this that the present focus is on forecasting and EW. Forecasting of DSS involves several
22   aspects: (i) prediction of DSS onset (outbreak); (ii) prediction of transport routes and time of arrival at
23   downstream sites. It depends to a large extent on the meteorological data routinely collected by the
24   national meteorological administrations but can be refined by collecting additional data that is specific to
25   DSS.
26
27   Early warning is valuable to several end users: policy makers and to industry and commerce; and to the
28   general public as part of a national advisory or hazard warning system.
29
30   Both forecasting and EW rely on the availability of quality data that is rapidly transmitted to those who
31   need it. Regional cooperation to facilitate data sharing will be a future development and the options for
32   this are detailed below.
33




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 1   7. Current monitoring program in the partner countries
 2
 3   Each of the four partner countries has a meteorological service. Each maintains a network of monitoring
 4   sites. All are partners in the WMO system and data are relayed in real time via the GTS communication
 5   system. These data are used to provide weather forecasts. Some countries have dedicated monitoring sites
 6   for collecting baseline data on air quality, including dust aerosols.
 7
 8   7.1 PRC
 9
10   The situation in PRC is complicated in that many different agencies and institutions maintain monitoring
11   networks within the nation. None was specifically set up to monitor DSS but many stations collect data
12   that is relevant to forecasting and early warning. Access to the data within the time frame required by
13   modelers and forecasters is not always possible.
14
15   In PRC there are at least four institutions at the central governmental or institutional level directly
16   responsible for the prevention and control of DSS, especially for the monitoring, forecasting and early
17   warning (Table 3). These are the State Environment Protection Agency (SEPA), The Central
18   Meteorological Agency (CMA), The State Forestry Agency (SFA) and The Chinese Academy of Sciences
19   (CAS). Each has its own network.
20
21   The principal monitoring agencies are CMA, SFA and SEPA. These three organizations need to employ
22   the full range of resources at their disposal to establish an integrated DSS monitoring network that is able
23    to conduct real-time monitoring. This network should employ recognized indicators and generate
24   standardized data sets acceptable to Mongolia, PRC, Korea and Japan.

                                                                   DSSFS
                                                            DSS Forecasting System



             Weather Model-MM5                                      DAM                                   Data Assimilation System
        Chinese Weather Forecast Model                       Dust Aerosol Module                           Dust Initial Condition



       Dynamics           Tracer Transport       Source Function          Aerosol Processes   Surface-based Obs.        Satellite-based Obs.
        Physics                                                                                Absolute Values                 Saptial




                                                               DSSEWS
                                                        DSS Early Warning System



                                             TV programs                             Public
                                             DSS bulletin                Decision-making Agency of Gov.
                                             Website etc.


                                Figure 5 The DSS fo recasting system and early warning system in Ch ina

25   CMA maintains a network of over 2000 stations, with 60 in the DSS source areas. CMA developed a
26   forecasting and EW scheme (Figure 5) that became operational in May, 2001
27
28   Major activities include construction of relevant institutions and development of human resources,
29   establishing information-system on collecting, analyzing, on-line monitoring and the issue of regular
30   relevant information bulletins, setting up data-bank and developing software, constructing different
31   observation-sites, and so on. Each institution has independently sought to develop FC and EW capacity.
32
33   CMA has since 1 March, 2001 decided to input the results on forecasting (for the normal sandstorms) and
34   early warning (for the stronger sandstorms) into the regular meteorological forecasting-system. The first
35   early warning for the stronger dust and sandstorms was issued on 18 March 2001 in the parts of Gansu
36   province and Inner Mongolia and Ningxia Autonomous Areas with some indicators about stronger-wind



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 1   and lower-temperature. They are now developing the so-called ―the state services system for monitoring
 2   and early warning sandstorms in the first-stage‖ which involves a lot of data concerning the formation
 3   (outbreak), movement and distribution of dust and sandstorms (see section z below). CMA issues the
 4   forecast of DSS events after analyzing the main meteorological indicators such as wind, visibility and
 5   radiation collected from about 60 meteorological observation-sites in north and north-west PRC.
 6
 7   SEPA launched a research-program on dust and sandstorms in August, 2000 and set up the first ground-
 8   network for monitoring dust and sandstorms covering Inner Mongolia, Hebei, Shanxi, Xinjiang, and
 9   Gansu. SEPA had about 40 observation-sites in PRC at the beginning of 2001. Most stations are in the
10   bigger cities, but not all. The chief tasks for these observation-sites are collecting samples of sands and
11   dust to help identify source areas and reporting meteorological change that is conducive to DSS. Two
12   types of data are collected: TSP (Total Suspended Particulates) and Lidar ( Figure 6). SEPA also collects
13   relevant basic data such as the sand-dust’s size, mineral composition, chemical composition, storm
14   duration and intensity and the path for long range transport of dust.




15
16
17   7.1.1.Data sources in PRC with potential value in DSS monitoring
18   There are numerous data, and several databases available in PRC related to DSS. Table3 is a summary of
19   these. We need a reasonable data sharing policy, mechanism, and facility to make the data sharing
20   realizable, accessible, operable and feasible (below). It is clear that the priority must be on getting the
21   maximum value out of the existing data bases and on getting access to data in real time (where necessary
22   for forecasting and prediction).




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1   Table 3. Overview of Data sources in PRC with relevance to DSS
2
       Organization            Human              Contact Information    Data,,                      Availability            Requirement        Cost
                               Resources                                 Database
       National Satellite      10 experts         Mr. Luo Jingning       GMS images                  Open to public,         DSS real time      Free
       Meteorological                             Data Service Center,   NOAA images                 Can be download         monitoring
       Center, China                              National Satellite     DSS density map retrieval   from
       Meteorological                             Meteorological         from                        www.dear.cam.gov.cn
       Administration                             Center, CMA            NOAA image
                                                                         Visibility retrieval from
                                                                         NOAA image
       Ministry of Land and    More than 100      Mr. Sha Zhigang        Based on Landsat TM,        Internal Use            Information on     Need
       Resources               experts involved   Department of          SPOT                                                desertification,
                                                                                                                                                Negotiation
       (Project on             in the project.    Cadastral              integrated with ground                              land
        Remote Sensing for     About 20 of them   Management,            survey                                              degradation,       With
       Land                    are remote         Ministry of            Output: 1:10, 000, 1: 50,                           rangeland
                                                                                                                                                MLR
        Resources and          Sensing experts,   Land and Resources     000,                                                degradation,
        Ecological             others are                                series thematic map                                 land
       Environment             Involved in                               Land Use Map                                        salinization in
       Monitoring in           Ground survey.                            Ecological Environment                              the source
        Beijing Surrounding                                              Map                                                 region and
       Regions. Three                                                    Land Degradation Map                                the transport
        Organizations within                                             Vegetation Map                                      path are
       MLR                                                               Soil Map                                            essential for
        Involved in this                                                 Slope Grade/Slope Aspect                            DSS
        Project)                                                         Map                                                 monitoring and
                                                                         Standard Image Map                                  early warning.
                                                                         Monitoring Target:
                                                                         Cultivated Land
                                                                         Degradation
                                                                         Rangeland Degradation
                                                                         Land Salinization
                                                                         Wetland Change




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    State Forestry                                 Mr. Zhou Weidong       Mainly based on ground         The primary data and     Information on     Need
    Administration                                 China National         survey, using Landsat TM       database are only for    distribution of    Negotiation
    (National Project on                           Desertification        images as the base map to      internal use. The        desert and         With
    Sand Land and                                  Monitoring Center      monitor desertification        statistics data are      desertification,   SFA
    Desertification                                                       produce the desert and         open for external use.   severity of
    Monitoring)                                                           desertification map and                                 desertification
                                                                          build up the desert and                                 are essential
                                                                          desertification database in                             for DSS
                                                                          1:100,000 scale.                                        monitoring
    Cold and Arid             58 expert are        Dr. Wang Tao           Based on Landsat TM            1:200,000 scale, and     High               Need
    Regions Environment       involved in desert   Key Lab of Desert      image to monitor the           1:500,000 scale maps     resolution         Negotiation
    and Engineering           and                  and Desertification,   desertification evolution      are for internal use.    information on     With
    Research Institute,       desertification      CAS                    and tendency in Northern       1:4,000,000 scale map    distribution of    CAREERI,
    CAS (Research on          research             Division of Desert     China.                         will be published and    desert and         CAS
    Desertification Process                        and Desertification,   Output:                        available for public     desertification,
    and Prevention in                              CAREERI, CAS           Map of Desert and              use soon.                severity of
    Northern China,                                                       Desertification Distribution                            desertification
    National Fundamental                                                  in China in                                             are essential
    Research Program                                                      1:200,000 scale,                                        for DSS
    (973))                                                                1:500,000 scale and                                     monitoring
                                                                          1:4,000,000 scale
    Beijing Digital View      Unknown              Mr. Zhuang Dafang      1. National Resource and       For sale:                Approximately      850,000
    Ltd                                            Beijing Digital View   Environment Database           1. 1000 RMB per          need 850 scene     RMB for
                                                   Ltd.                   2. 1:100,000 scale and         Land Use map in          Land Use           100,000 scale
                                                   Tel: 86-10-82332473    1KM grid land use              100,000 scale            Maps               maps
                                                   Fax: 86-10-82332472    database                       2. 5000 RMB for
                                                                          3. 1KM grid Ecological         1KM grid Land Use
                                                                          Environment Database           Map
                                                                                                         3. 12000 RMB for
                                                                                                         1KM grid Ecological
                                                                                                         Environment Map
1
2
3
4
5
6
7
8



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1   Table 4. Status of permanent observation stations maintained by State Forest Administration of PRC
2
    Stations    Starting     Location         Purpose                         Normal factors for observation                  Facilities
                  year                                                                                                         needed
    Cele, XJ   1998        S Edge,         Dust disaster      Meteorolo     Soil         Vegetation     Dust weather
                           Taklimakan      and dune           gical
                           desert          movement
                                           monitoring
    Shazhuyu   1998        Gonghe Basin    Impact of dune     1.Local       -erosion;    -Prevailing    -Wind              -Light-
    QH                                     reactivation and   Meteo         -moisture    species;       velocity;          electronic dust
                                           range land         factors;      -Texture     -Coverage;     -Dominant          collectors;
                                           degradation to     2.Auto        -organic     -Height;       wind               -Neutron
                                           Longyangxia        Meteo         matter –     -Density;      direction;         moisture
                                           Dam                Observati     -Nutrient    -Biomass.      -Visibility;       meters;
    Minqin,    1998        E Edge,         Wind-sand          on            status                      -Continuity        -Dust
    GS                     Baidan Jilin    harm and water     system, if    Porosity.                   of dust;           sediments
                           Desert          quality change     there is no                               -Times and         collector and
                                           in Hexi            meteo                                     intensity of       spare parts;
                                           Corridor           station                                   dust               -Ground Auto
    Yanchi,    2000        SW edge of      Dust weather at    nearby                                    occurred.          Meteo
    NX                     Mu Us           marginal and                                                                    observation
                                           transitional                                                                    system
                                           areas
    Dengkou,   1998        E of Ulan Buh   Dust frequency,
    IM                     Desert          dune
                                           movement,
                                           vegetation
                                           degradation and
                                           secondary
                                           salinization
    Ulan       1998        Centre of       Dust weather
    Aodu, IM               Horqin Sandy    and
                           Land            revegetation




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    Yulin,    1998       SE of Mu Us     Wind-sand
    ShX                                  status and
                                         impacts of
                                         mining and
                                         steppe
                                         degradation
    Huang     2001       Low part of     Dust weather in
    Yangtan              Hebei Upland    Yanshan Basin
    HB                                   and monitoring
                                         effect of
                                         Beijing-Tianjin
                                         Project to
                                         Control Dust-
                                         sand Storm
1
2   Notes: XJ=Xinjiang, GS=Gansu, QH= Qinhai, ShX=Shaanxi, IM= Inner Mongolia, HB=Hebei




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 1   SFA is the institution whose mandate is to prevent and control desertification in PRC. It maintains a
 2   National Desertification Monitoring facility (Table 3). It has a network of 30 monitoring stations in
 3   northern and western PRC. In addition, it has a special responsibility to mitigate the impact of DSS in
 4   PRC (Table 4).
 5
 6   SFA launched a project on monitoring wind-sand resource in five provinces around and affecting Beijing
 7   and Tianjing municipalities on 11 April 2002. The monitoring system of three north shelterbelt program
 8   have also been established to monitor vegetation recovery and project effectiveness. SFA monitoring is
 9   different from other systems and consists of ground surface monitoring (soil, vegetation, land use change),
10   onset and spread of desertification, the supervision of control-sand-project and the evaluation of sand
11   control projects. Recently, SFA has been constructing a new system with the goal of short-term
12   forecasting of DSS events, based on the monitoring the duration, severity and processes of DSS. Disaster-
13   assessment for DSS is also included in SFAs program. Remote sensing technology and ground monitoring
14   technology are combined.
15
16   CAS began in 2002 to establish a monitoring-network on dust and sandstorms in 7 provinces of northern
17   China with 12 observation-site developed since 2002. CAS now plays an important role in prevention and
18   controlling from dust and sandstorms nationwide in theoretical fields. These observation-sites collect
19   relevant information about DSS and record meteorological change day by day by means of using some
20   modern equipment e.g. laser radar monitoring-dust instrument (Lidar). CAS scientists now play an
21   important role in understanding the principles required to prevent and control DSS.
22
23   In summary, all the institutions mentioned above have their own individual network and whilst they are
24   achieving positive outcomes this approach to a national and regional problem is not so efficient or cost-
25   effective.
26
27   7.2 Korea
28
29   7.2.1 Dust monitoring by the Korea Meteorological Administration
30   Korea is subjected to periodic DDS that can involve both blowing dust and floating dust. DSS monitoring
31   sites are maintained on Korea’s west coast and throughout the country (see map). Both TSP and Lidar data
32   are collected. Both the KMA and the Ministry of Environment (MoE) are involved in monitoring. The
33   emphasis in MoE is on air quality.
34   KMA observes and monitors Asian dust events in following ways:
35        1) Observation with naked eyes for the occurrence of Asian Dust event at 42 synoptic weather
36        stations.
                                                  37     2) PM10 measurement at seven sites (Anmyondo and
                                                  38     Baek-Ryeongdo located in the western part of the
                                                  39     Korean peninsula, and Mt. Gwanak in Seoul, Gunsan,
                                                  40     Incheon, Heuksando, Gosan) and five sites
                                                  41     (Gwangduksan, Chupungryung, Gwangju and 2 more
                                                  42     sites will be determined in 2004 (Fig. 2).
                                                  43     3) Lidar measurement at the GAW observatory
                                                  44     (Anmyondo) for the vertical distribution and one more
                                                  45     site will be added in 2004.
                                                  46     4) Modified Satellite images from GOES, NOAA,
                                                  47     TERRA, SeaWiFs
                                                  48
                                                  49     Fig 7. PM10 monitoring network managed by Korea
                                                  50     Meteorological Administration. Black circle means
                                                  51     the already operating sites and white one the new
                                                  52     additional sites in 2004.




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                                                    Prevention and control of Dust and sand storms in North East Asia
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 1
 2   Fig.8 . Monitoring network managed by Ministry of Environment in Korea. MOE maintains 188
 3   automatic air pollution-monitoring stations to measure the atmospheric pollutants including dust (PM10)
 4   in the air.
 5
 6   KMA provides routine weather forecasts but also an EW Advisory service (Box 3).
 7
 8   Box 3 Republic of Korea’s DSS Advisory and warning service
 9

                 Watch                            Advisory                          Warning

     When an hourly averaged dust      When an hourly averaged dust      When an hourly averaged dust
     concentration (PM10) expects to   concentration (PM10) expects to   concentration (PM10) expects to
                  /㎥
     exceed 300㎍ for over two                       /㎥
                                       exceed 500㎍ for over two                        /㎥
                                                                         exceed 1000㎍ for over two
     hours                             hours                             hours

10
11   7.3 Japan
12
13   There is an extensive network of meteorological stations throughout Japan, maintained by Japan
14   Meteorological Administration (JMA) In addition to this network there is also a series of air monitoring
15   stations (including DSS monitoring) maintained by the Ministry of Environment (Figure 9). TSP and
16   Lidar are the principal indicators but PM10 and other parameters are measured. Air quality considerations
17   are the principal focus because floating dust is the main problem.
18




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 1
 2
 3   Figure 9 Lidar and TSP monitoring stations in Japan
 4
 5   7.4 Mongolia
 6
 7   The principal monitoring stations are maintained by the Ministry of Nature and Environment and data
 8   sharing is easier to arrange. The National Agency for Meteorology, Hydrology and Environmental
 9   Monitoring (NAMHEM) operates a network for meteorological survey that consists of 117 meteorological
10   stations and 185 meteorological observation posts (Figure 8). Of these, 40 stations report their data to the
11   WMO.
12
13   The main objectives of the network are (i) to provide survey information on the weather condition to the
14   relevant organizations and public; (ii) to inform daily, 10 days weather monitoring data as well as warning
15   information on emergency and extreme emergency phenomena to the weather forecasting department in
16   accordance with the established rules.
17
18   Currently, in Mongolia are 117 weather stations, 219 meteorological posts, 7 upper air sounding stations,
19   22 air pollution controlling stations, 8 environmental monitoring stations, 1 satellite receiving station, 1
20   weather radar observation station(figure 10 ). All the observation data from these stations is collected in
21   Information Center (ICC), Ministry of Nature and Environment. After checking, sorting and primary
22   processing data is transmitted to Institute of HydroMeteorology, provincial meteorological centers and
23   WMO regional centers (through Beijing and Novosibirsk).
24
25   The meteorological sites in Mongolia measure the following DSS related parameters.
26           -    Dust storm(duration, maximum wind speed)
27           -    Wind speed and direction (8 times per day)
28           -    Visibility at daytime
29           -    Soil moisture at 22 points during the spring
30           -    Surface temperature at 0800 and 2000 hrs
31   Only 15 stations have wind measurement instrument named "M-63", in other stations wind observation is
32   visual. Visibility is defined by observer's sight. Upper air stations measure wind speed and direction at
33   levels up to 13 km, sounding twice per day at 00 and 24 hrs (GMT).
34
35   Although there are 22 air quality monitoring stations in the country, the measurement of TSP is carried out
36   only at 1 station in Ulaanbaatar, due to financial constraints. There are no stations for dust monitoring
37   along the path taken by DSS events.



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 1
 2   The Civil Aviation Authority (CAA) has its own Airport Surface Meteorological Observation and Data
 3   Acquisition System that consists of meteorological data collectors for its airdrome needs in the 18 airports
 4   that are located in aimag (province) centers. Seven airdromes at the aimag centers are adjoined to the
 5   meteorological observation fields of NAMHEM, therefore the real-time monitoring data of these
 6   automatic stations can be used also for weather stations if the communication problem is solved.
 7
 8   The main problem in Mongolia is lack of a good data-base. Since 1936 the meteorological observation
 9   system has been developed and expanded. At present, the existing ground meteorological monitoring
10   network in Mongolia is virtually unavailable to meet the DSS monitoring needs because of:
11          - Insufficient density of ground meteorological stations, especially in remote and desert area
12          - Measurement of wind and visibility are not instrumental, visual
13          - Out of date observation instruments
14          - Poor infrastructure of stations, many stations don’t have power supply
15
                                                          16     Although the highest proportion (~30% (10%)
                                                         (a)
                                                         17      during the last 43 years) of DSS sources are located
                                                          18     in Mongolia[Zhang et al., 2003], there are no special
                                                          19     monitoring sites for DSS. Most meteorological
                                                          20     stations in Mongolia (Figure 9) don’t have any
                                                          21     direct relation to DSS. Table g in section 14 gives
                                                          22     details of the status of each meteorological site in
                                                          23     Mongolia. Mongolia has only one satellite receiving
                                                          24     station that receives SeaWiFs and AVHRR data
                                                          25     from SeaDas and NOAA satellites. Methodology of
                                                          26     using NOAA data for DSS monitoring is in the
                                                          27     developing stage. But they output some information
                                                          28     on DSS indicators (desertification and drought,
                                                          29     snow cover, land surface temperature) See Figure 8
                                                          30
                                                          (b)

                                                                                                                (c)




                                                       31
32   Figure 10 Monitoring stations in Mongolia (a) Network of meteo stations maintained by NAMHEM (b) yearly
33   average number of days with dust storms observed based on horizontal visibility in Mongolia during 1937-
34   1999[Natsagdorj et al., 2003] (c) Network of ecological stations. Note the distribution of dust days in relation to the
35   existing network of monitoring sites
36
37
38
39




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 1   8. Indicators for monitoring DSS: its outbreak and transport
 2
 3   8.1 Major monitoring indicators and analysis methodologies
 4   It must be remembered that each partner country has a different perception of DSS and experience
 5   different degrees of impact from DSS events. Therefore the most appropriate and useful indicators may
 6   vary from country to country or even from place to place within a large country, like PRC. In the source
 7   areas and the surrounding regions that receive massive deposition the outbreak of the DSS event and its
 8   duration and geographic extent are key factors whilst in the receiving areas the full spectrum of
 9   phenomena can occur (deposition, loss of visibility, and a serious deterioration of air quality). What
10   follows is an annotated list of indicators but it must be remembered that not all will be relevant to all
11   situations. Some though have special value as input for modeling and forecasting.
12   “Dust emission flux” is one of the good indicators for DSS FS and EWS. Information on the quantity of
13   dust produced is the most important and direct indicator for identification of occurrences of DSS. The
14   dust emission flux data can be derived from historical and empirical data as part of the existing FS. It
15   would provide an early identification of the outbreak of DSS.
16
                                                                                                                                   (a)




       Figure 10. Time series of TSP and visibility (14:00 observation)
       in XiAn from September 1996 to August 1997[Zhang et al., 2002]

17   “Dust aerosol loading” can be considered as the best                                                                          (b)
18   indicator for DSS regional monitoring and early
19   warning network observations, because the higher dust
20   concentration observed especially in depositional
21   regions of DSS exhibits the transported dust associated
22   with DSS from the source regions. If the spatial
23   distribution of dust aerosol concentration predicted by
24   FS can be compared and adjusted quickly and
25   efficiently with an observed distributions of dust
26   concentration from the regional network stations. A
27   new initial condition will then be used into the rolling
28   forecasting, which is extremely important for the
29   accuracy in forecasting. Unfortunately no dust                                                                                (c)
30   concentration data can be quickly and efficiently
31   transferred to the existing DSS FS and cannot be
32   established in the near future. This is mainly because to
33   obtain the dust aerosol concentration will take to
34   conduct the chemical analysis that will usually take a
35   several days or weeks.
36
37    “Horizontal Visibility” can be considered as an
38   effective / optimal alternative. This is because the
39   visibility observation and analysis have been used in
40   classifying and reporting [Middleton and al, 1986;
41   Natsagdorj et al., 2003] DSS in almost all the countries                   Figure 11 Spatial distribution of DSS provided new
42   at least since the last 50 years. Another important thing                  dust initial condition for accu rate forecasting. (a)
43   is that the value of horizontal visibility has a                           DSS distribution,, ret rieved fro m FY-IC satellite at
44   relationship with surface dust concentration (e.g.,                        CMA (08:00 BST, 20 March 2002; (b) DSS
                                                                                distribution reported on the basis of horizontal
                                                                                visibility in China (20:00BTS, 19-March to 05:00,
                                                                                20 March2002); ( c) FS estimated surface
                                                                                concentration of dust aerosol (µg m-3 ) and observed     31
                                                                                wind and rain (02:00 BST, 20 March 2002) at Ch ina
                                                    Prevention and control of Dust and sand storms in North East Asia
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 1   Figure 11 for instance), and can then somewhat provide the alternative dust distributions almost in real
 2   time. Moreover, quick obtaining (four time report each day) and efficient communication system (e.g.
 3   GTS) do exist in almost all the countries that are very useful in validating the DSS FS output. WMO has
 4   been using visibility classifying the DSS till present-day. The only problem for visibility network is that
 5   all the existing visibility data coming from naked eye, not instrument.
 6
 7   “Total suspended particle (TSP)” is another proxy indicator for DSS FS & EWS. Referring TSP as a
 8   third proxy indicator is mainly because TSP is not a pure dust aerosol concentration indicator. TSP over
 9   continental Asia can actually be divided into five general categories: (1) soil dust aerosol and associated
10   species (consisting of oxides of Al, Si, K, Ca, Ti and Fe, and trace elements), (2) particulate sulfates, (3)
11   aerosol nitrates, (4) ammonium products, and (5) carbonaceous material [Solomon et al., 1989; Zhang et
12   al., 2002; Zhang et al., 2001]. One cannot be simply attribute the observed TSP to the dust contributions
13   and the associated DSS event. The other four types particulate all contribute to the observed TSP. Another
14   problem for TSP is that there is no real-time TSP data can be obtained by instruments at this moment all
15   over the world. Once there is a DSS outbreak, a High Volume Air Sampler can be employed for TSP
16   measurement and DSS sampling.
17
18   “Particulates with diameter smaller than 10 µm (PM10)” It has been listed as one of the indicators for air
19   quality evaluation in every country; also it is a main indicator to estimate the impact of suspended
20   particulate matter on human health. A major characteristic of DSS in Northeast Asia is its color. The color
21   of PM10 sample collected from the middle and east of PRC, Korea and Japan is darker than TSP sample.
22   The color is a clue as to the source area. Currently, β-ray dust mass monitor is employed to PM10
23   measurement in PRC.
24   This indicator is also somewhat useful, but it is less important than TSP. It is not only because the
25   contribution to the PM10 loading are from five types of particulate, but also because the soil dust particles
26   associated with DSS include lot of particles with diameter larger than 10 µm dust particles. This would
27   give a gross underestimate, especially in severe or very severe DSS events. But the PM10 data can be
28   obtained in real-time in the proposed network stations, a merit over TSP.
29
30   Vertical profile of Dust Cloud (Lidar monitoring)
31   The three operational indicators, visibility, TSP, PM10, listed above are all employed to monitor
32   particulate concentration close to the surface. The vertical distribution is also important information for
33   validating the FS output in dust concentration at different levels of atmosphere. But most Lidar system
34   cannot work effectively under severe DSS conditions, and are very expensive as well.
35
36   Satellite retrievals are also helpful in reconstructing the dust distribution. That why there is satellite
37   component in the Data Assimilation System of the DSS FS & EWS (Figure 12, for instance), though it’s
38   difficulties to quantify dust optical thickness and dust loadings from satellites, however, due to
39   uncertainties in assumptions concerning particle size, refractive index and particle shape. Interference
40   from cloud cover and the limited temporal coverage of the satellites present further limitations, but
41   satellite retrievals can also be considered as one of the effective ways of reconstructing the general
42   magnitude and the spatial/temporal patterns of aerosol. Other good points of the satellite are the
43   capacity of obtaining real-time data and retrieval of the surface soil moisture. The surface
44   observation of soil moisture is also important for comparing it with satellite retrieval (see section 9).
45
46   The most frequently used indicators and technologies in DSS monitoring are Visibility, TSP, PM10 and
47   Lidar. These indicators should form the core of any monitoring program. But knowing what data to collect
48   is only the first step. Collecting it in a way that allows cross-region comparison is another. Box 4 sets out
49   some indicators that should be considered in the DSS monitoring in the future.
50
51   “Dust emission flux” is one of the good indicators for DSS EW and FS. Information on the quantity of
52   dust produced is the most important and direct indicator for identification of occurrences of DSS. The
53   dust emission flux data can be derived from historical and empirical data as part of the existing forecasting
54   system. It would provide an early identification of the outbreak of DSS.
55




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                                                   Prevention and control of Dust and sand storms in North East Asia
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 1   Box 4 Other DSS indicators
 2
 3         Relief
 4           - Slope
 5           - Aspect
 6           - Elevation
 7         Soil
 8           - Texture
 9           - Moisture
10           - Temperature
11           - Bare soil
12         Vegetation
13           - Coverage
14         Meteorological
15           - Wind velocity, direction
16           - Air turbidity
17           - Visibility
18           - Atmospheric stability parameters
19           - Snow coverage
20         Socio-economic
21           - Unpaved road network
22           - Open mining area
23
24   9. Ground surface monitoring: its role and effectiveness
25
26   Dust and Sandstorms are atmospheric phenomenon and are often classified as hazardous weather events.
27   However, they are ground-initiated and ground-generated in source regions, ground-enhanced along their
28   transport path, air-driven storms. The ground surface conditions and the ecological environmental of the
29   source regions and along the transport route are very important.
30
31   Remote sensing has value in DSS monitoring in two main ways:
32        Real-time monitoring in spatial distribution of DSS by remote sensing
33        Ground surface conditions and ecological environmental assessment in the source regions and
34            depositional regions by remote sensing
35   Low-resolution remote sensing data can be used to retrieve the spatial distribution of DSS, aerosol optical
36   depth and aerosol index when a DSS event occurs. The framework of remote sensing technology for DSS
37   monitoring is shown in Figure 12. The satellite data and models used for DSS monitoring are shown in
38   table 5.
39
40   The monitoring should focus on the source region, the transport path and the deposition areas. Low
41   resolution remote sensing data can be used to identify the outbreak, extent, density, and visibility during
42   the DSS events. The framework of remote sensing technology for DSS monitoring is shown in Figure 13.
43   The satellite data and models used for DSS monitoring are shown in Table 5.
44
45   9.1 Remote Sensing for DSS real time monitoring
46        Using GMS satellite data, every hour monitoring during the DSS season, from February to June.
47        Using NOAA AVHRR satellite data for DSS, soil moisture, vegetation monitoring, and every day
48          monitoring during the DSS season, from February to June.
49        Using SPOT VEGETATION for Vegetation monitoring, every ten days during non DSS season,
50          every one day during DSS season, from February to June.
51        Using MODIS for vegetation monitoring and soil moisture monitoring every day during the DSS
52          season from February to June.
53
54
55



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 1   Table 5. Satellite data and models for DSS monitoring
 2
      Satellite         GMS/VISSR               NOAA AVHRR                      MODIS
      Frequency of      Hourly                  Twice a day                     Twice a day
      visit
      Resolution        5 km                    1.1km                           250m, 500m, 1km
      Swath             1/3 of the earth        2000km                          2330km
      (coverage)        surface
      Selected          Visible, infrared       1.6µm                            1.628-1.652µm
      wavelength                                11µm                             3.66-3.84µm
                                                12µm                             10.78µm
                                                                                 11.28µm
      Selected bands    All bands           Channel 3A, 4 and 5                  Channel 6, 20, 31, 32
                                                   ( bvch 3 )
      Models for best   No            NDDI  a  e              (ch 4  ch5) / ch 4 Not available, can use
      observation                                                                       same model as NOAA
                                                                                        data
      Data cost         Free, material cost Free, material cost                  Free, material cost
      Station cost      200,000             200,000                              3,000,000
      (RMB)
      Output            Hourly monitoring       Daily Monitoring                Daily Monitoring
                                                1. Comparable            dust   1. Comparable dust intensity
                                                    intensity index                 index
                                                2. Visibility                   2. Visibility
                                                3. Monitoring the source,       3. .Monitoring the source,
                                                    transportation, diffusion       transportation,  diffusion
                                                    and deposit of dust             and deposit of dust
                                                    sandstorms                      sandstorms
                                                4. Albedo*                      4. Albedo*
                                                5. Optical Depth**              5. Optical Depth**
      Comments          Limited by cloud        Limited by cloud cover          Limited by cloud cover
                        cover
 3
 4   * Albedo: Percentage of light reflectivity (radiance), as in preface white =100% Albedo
 5   **The optical depth model is available but the model validation is a difficult to conduct. It needs highly restricted
 6   conditions. The model is ready to serve the research purpose but cannot meet the requirements in operation. It is a
 7   valuable parameter for DSS monitoring, but it is not sufficiently precise for quantitative analysis. It needs further
 8   research and supporting instruments.
 9
10   Remote sensing has an important role for ground surface conditions and ecological conditions assessment
11   in the source regions and along the transport path. Tables 5,6 & 7 show the attributes of the various
12   satellites in common use.
13
14   Remote sensing has great potential for DSS monitoring. A framework of how remote sensing technology
15   might be integrated into DSS Monitoring and Early Warning in PRC is shown in figure 9.
16
17   DSS events are is not stationary and change is rapid. For real-time monitoring at regional scale, high
18   resolution satellite data will not fit the purpose due to the narrow swath width(60-180km) and low
19   temporal resolution (16 -26 days). Also high-resolution data is expensive.
20
21   For this purpose the most suitable remote sensing satellites are:
22            1. NOAA. AVHRR data successfully has been used for environmental studies and monitoring at
23                the regional scale. The advantages of NOAA data is its availability at every 6 hour interval
24                and at 5 bands 0.58 - 0.68, 0.725-1.1, 3.55-3.93, 10.3-11.3, 11.5-12.5 mkm over the large
25                area. Also NOAA data is cheap and is very useful for long term monitoring. All participating
26                countries have a NOAA receiving station.
27            2. MODIS provide comprehensive coverage in spectral and spatial contexts in comparison with
28   NOAA data. Spatial resolution of 250, 500 and 1000 m in 36 wavebands in the spectral range from 0.4 to
29   14 mkm. Temporal resolution is 1-2 days.



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 1
 2   Another suitable sensor for DSS is Microwave sensors. Microwave RS has one very good advantage of
 3   cloud transparency observation. In most of cases DSS especially those caused by cyclone, coincides with
 4   cloud. This sensor gives us the possibility to monitor even in the case of cloud cover. Microwave RS has
 5   been successfully used for soil moisture and precipitation monitoring, both important indicators of DSS.
 6
 7   High resolution data can be used in limited key DSS source areas, where DSS occurs more frequently and
 8   has more serious affect. Geostationary satellites that provide data every 30 minutes have been mostly used
 9   for weather forecasting. Also, combined and integrated use of multi-sensors of different satellites could be
10   useful.
11
12             Table 6 List of DSS indicators derived from satellite
     Satellite name              DSS indicators                                  Usage status
     NOAA                        Vegetation cover                                operational
                                 Land surface temperature
                                 Wind
                                 DSS location, movement
                                 Land cover type
     MODIS                       Land cover type                                 experimental
                                 Aerosol thickness, size distribution
     SeaWifs                     Aerosol thickness, size distribution            experimental
     Landsat                     Vegetation cover                                operational
                                 Sandy sources
                                 Relief
                                 Land cover type
     SPOT                        Vegetation cover                                operational
                                 Land cover type
                                 Sandy sources
                                 Relief




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1   Table 7. Remote sensing for monitoring of ground surface conditions
2
       Satellite       NOAA AVHRR                      MODIS                      SPOT                       SPOT                   LANDSAT TM
                                                                                  VEGETATION
       Frequency       Twice a day                     Twice a day                One day globe              1-4 days               16 days
       of visit                                                                   coverage
       Resolution      1.1km                           250m, 500m, 1km            1.15km                     2.5m, 5m, 10m,         15m, 30m
                                                                                                             20m
       Swath           2000km                          2330km                     2250km                     60km*60km,             180km*180km
       Vegetation      Band 2,1                        Band 2, 1                  Provide one day            Band 3,2               NDVI=(IR-R)/(IR+R)=
       coverage                                        NDVI                       synthesis vegetation       NDVI                   (TM4-TM3)/(TM4+TM3)
       monitoring                                      VI                         index image and 10         VI                     VI=IR-R=TM4-TM3
                                                       IR/R                       day synthesis              IR/R                   IR/R=TM4/TM3
                                                       SQRT IR/R                  vegetation index           SQRT IR/R
                                                                                                                                    SQRT IR/R= TM 4 / TM 3
                                                       TNDVI                      image                      TNDVI
                                                                                                                                                 TM 4  TM 3
                                                                                                                                    TNDVI=                    0 .5
                                                                                                                                                 TM 4  TM 3
       Selected        4, 5                            4,5,6,7,8,9                SWIR band: Leaf            No                     Band 6
       bands for                                                                  surface water content
       soil
       moisture
       Selected        4, 5                            MODIS Standard             All bands                  All bands              All bands
       bands for                                       Snow Products
       snow                                            MOD10
       cover                                           MOD33
       Cost            Free, material cost             300RMB/orbit               Annual fee 110,000         12000RMB/scene         5000 RMB/scene
       estimation                                                                 RMB
       Comments        Last winter snow cover          Regular monitoring         VEGETATION                 5 years                5 years monitoring circle, focus
                       monitoring is the most          from summer to next        NDVI data can be           monitoring circle,     on source region
                       Important for DSS               spring                     downloaded from            focus on source
                       monitoring                                                 www.vgt.vito.be            region
3
4   NOTES VI: Vegetation Index. VI>1, is the boundary of vegetation or non vegetation
5   NDVI: Normalized Difference Vegetation Index. NDVI>0.15, is the boundary of vegetation or non vegetation. This is a more precise and common used indicator for vegetation.
6   TNDVI: Transformed Normalized Difference Vegetation Index. If use this model, then there is no negative value will appear in the image no mater there is vegetation or other targets in the
7   image with higher reflectivity on red band than Infrared band. The input for these models must be the reflectivity, not the digital number of the images.




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 1
 2
 3
 4                                              The Source,                   Forecast to Public and                         The Source,
 5                                                Possible                    Government Authorities                          transport,
 6                                             transport path                                                                 deposit of
 7                                                of DSS                                                                         DSS
 8
 9
10
11
12
13                                                      Early Warning                                         Monitoring
14
15
16
17                   Database
18              Meteorological data,
19              Soil texture,
20                                                 Statistics                 Vegetation Coverage                   Identification of DSS
                Elevation and Terrain,
21                                                 data,                      Vegetation Health Soil                Outbreak, Extent,
                Ground Surface                     Publicized                 Moisture
22                                                                                                                  Density, Visibility
                Conditions                         data                       Snow Cover
23
24
25
26
27
28                      Ground             Landsat TM               MODIS           SPOT                MODIS            GMS/              NOAA/A
29                      Condition
                                           SPOT                                     Vegetation                           VISSR             VHRR
30                      Surveying
                                                                    AVHRR                               AVHRR
31
                                                                                                                         AVHR
32                                                                                                                       R
33
34                                       National               Ministry of         CAREERI,
     Figure 13. Framework of                                                                                National Satellite
35                                       Desertification        Land &              IRSA/CAS
     Remote Sensing                                                                                         Meteorological Center/CMA
36                                       Monitoring             Resources
37   Technology for DSS                  Center/SFA
38   Monitoring and Early
     Warning as used in PRC



                                                                                                                                                                     38
 1   10. Why is a regional network needed: Rationale and justification for the DSS network
 2
 3   Considering the complex nature of the issue (scientific, political, socio-economic and the spatial and
 4   temporal components), a phased program for establishing an integrated regional monitoring and early
 5   warning network for DSS should be sought as the top priority. The benefits of such a network will flow to
 6   the partner countries. The merit of cooperating is that it will be possible to achieve much more through a
 7   network than by each country acting alone. Trans-boundary problems can most effectively be solved
 8   through cooperation. There is considerable value-adding when neighbors combine their efforts to combat
 9   DSS. Early warning of impending DSS events will be facilitated by data sharing and rapid
10   communications on the progress and geographic extent of any DSS outbreak.
11
12   The governments of PRC and Mongolia have formulated comprehensive programs to combat land
13   degradation and desertification, which serve as their main thrust to alleviate DSS. However, the linkage of
14   these national initiatives to the regional concern of transboundary DSS is yet to be established through
15   cooperation beyond national borders. Without an effective policy and coordination at a regional level, the
16   effectiveness of the national initiatives will be limited.
17
18   The intensified transboundary DSS has mobilized very strong public will for regional cooperation on
19   combating DSS, particularly in the DSS-affected areas, where hundreds of millions of people have been
20   exposed to the impacts of DSS on living standards, public health, and economic wealth. In addition to
21   various initiatives of the governments, nongovernment organizations and volunteers from the DSS-
22   affected countries have been actively undertaking cross-border activities to mitigate DSSs (e.g., planting
23   trees in the DSS originating source areas), but in a sporadic and uncoordinated manner. Greater
24   cooperation and coordination (a programmatic approach rather than a project by project approach) could
25   speed up the work and be more cost effective.
26
27   Regional cooperation will maximize the effects of the initiatives from the private sector and civil societies
28   in the affected countries. Free flow of data, much of it in real time, could facilitate forecasting and early
29   warning. Transport models for prediction of DSS paths, transit time, duration and severity as well as the
30   geographic extent can all be enhanced through cooperation agreements between the source countries and
31   the receiving countries.
32
33   11. Objectives of the program
34   11.1 Goal
35    To minimize the adverse impacts of dust and sandstorms (DSS) in North East Asia
36
37   11.2 Overall Program Objectives
38
39   To establish and strengthen the DSS forecasting (FC) and early warning (EW) capacity of NE Asia by
40   upgrading, enhancing, and integrating the DSS forecasting and early warning capacity in each
41   participating country through establishing a regional network.
42
43   11.2.1 Outputs:
44              1. Develop a phased program to select and upgrade monitoring stations throughout the
45                   region that can serve the requirement for Forecasting and EW.
46              2. Propose an operational framework of the network
47              3. Propose an implementation schedule for network establishment
48              4. Develop a proposed financial plan to cover establishment costs and on-going
49                   Maintenance.
50
51   11.3 Operational mechanism of the proposed regional network
52
53   The setting up of the regional DSS network and the associated national monitoring and assessment
54   network for FC and EW will facilitate a deeper probe into causes of the DSS problem and, therefore,
55   establish a scientifically justified perspective for policy making at the national and regional levels.
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 1   Whilst it is a clear intention of the governments of the 4 partner countries to improve the level of regional
 2   cooperation on DSS it is also clear that the mechanism and operation of any data-sharing network needs to
 3   be worked out carefully.
 4
 5   This report examines the situation in the four partner countries and answers key questions (Box 5)
 6
 7   Box 5 Vital questions about the proposed regional DSS network for NE Asia
 8
 9      What are present arrangements for data sharing and networking on DSS-related matters in the NE
10       Asia region?
11              - What are the deficiencies in present system?
12    Why do we need a regional network for NE Asia?
13               - What is the rationale for developing regional cooperation?
14    What is the purpose of the network?
15               - What would the regional DSS network do?
16              - How could it be used in forecasting and early warning?
17    Who would use it and how?
18                - What conditions might apply to the use of the data supplied?
19               - Who would specify what data to put into the network?
20       What network configuration is best to achieve the stated purpose?
21   ________________________________________________________________________________
22   11.4 What is the purpose of the network?
23   The mission of the network is to provide a coordinated and effective mechanism to collect, analyze
24   forecast and disseminate information on DSS and provide early warning (EW) of impending DSS events.
25   Specifically:
26   a) Establish and maintain a regional network in the application of new information technologies and
27   space-based and related ground validation for the monitoring and assessment of DSS in the north east
28   Asian region.
29   b) To enhance institutional capacity building for FC and EW at all levels with a special emphasis on
30   national focal points database management and information sharing
31   c) To standardize the database and procedures for estimating the extent and severity of DSS events and to
32   predict and forecast the likely onset of serious DSS events.
33
34   A specific regional DSS network would facilitate the free exchange of selected data sets that would assist
35   all partner countries (and those in North and central Asia generally) in mitigating the effects of DSS
36   events. Better forecasting and early warning helps industry and commerce, reduces the hazard and allows
37   the population to take preventative measures to safeguard property and human health.
38
39   The main functions of a successful operational DSS network would be to:
40       a. Clarify sources areas, transport routes, and influence areas of DSS events. With access to the data
41          accumulated for years, spatio-temporal distribution of DSS events, the physics of long distance
42          transport of dust aerosols and the environmental impacts of each DSS will be clearly identified.
43       b. Provide the scientific information for early forecasting and warning and for validation of models
44          and simulations
45
46   The proposed regional NE Asian DSS Network would do several things (i). it would cause national
47   governments in each of the partner countries to review and evaluate their current monitoring efforts.
48   Hopefully too, it would lead to closer integration and cooperation among the various institutions and
49   agencies and lead to better data flow and standardization of data collection, handling and processing
50   procedures. (ii) Gaps in the present regional monitoring facilities will be identified and the opportunity to
51   prioritize the setting up of new or better facilities can be done in the context of how it might benefit all
52   regional partners (iii) private sector support and funding for system establishment and on-going
53   maintenance is likely to be enhanced if an integrated and operational network is in place that delivers
54   more reliable and timely forecasts and warnings and which later yields tangible results in controlling and



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 1   preventing DSS. (iv) Data sharing and closer cooperation between neighbors would allow technology
 2   transfer to occur faster and more efficiently. Software (e.g. prediction models) and hardware
 3   developments would be available more readily to partners and advances in technology disseminated more
 4   quickly.
 5
 6   NE Asia-DSS network represents a coordinated way to mitigate the impacts of DSS on the atmosphere, on
 7   agriculture, forestry, and industry. In addition, it provides potent evidence to support efforts to accelerate
 8   measures to prevent and control DSS.
 9
10   11.5 Centralized versus decentralized regional structure
11   Already the WMO has a system for linking meteorological stations in each country of the world. Data are
12   transmitted regularly to the WMO nodes within the NE Asian region. However, these are not specific to
13   DSS but much valuable data is already transmitted in real time to these WMO nodes. Modelers and
14   forecasters could specify which data sets they can use and negotiate access to the WMO network.
15
16   It seems more achievable in the short term to encourage closer integration of the monitoring already in
17   place in each national system. Getting integration and data sharing nationally would represent a big
18   advance on the fragmented and often disparate systems in place in some counties (notably PRC).
19
20   11.5.1 Centralized systems
21   This implies that all the relevant data collection agencies in each of the 4 countries are participating in full
22   data sharing. This would be costly and an administrative nightmare. Probably it is unnecessary because
23   too much data can be as harmful as too little. The processing and analysis is time consuming and slow.
24   What is needed is a set of criteria (indicators) that are readily available from each country and which can
25   be used as a basis for forecasting and early warning.
26   Many central facilities require special purpose-built infrastructure and equipment for collection,
27   transmission, analysis and storage of data. They are costly to maintain.,
28    Within the notion of a centralized system there is the quite reasonable suggestion that data flows (as it
29   does now) into a central national collection point. The data may be real time (as is required for weather
30   forecasting) or be sent to a national data bank for later analysis and to be part of an archive.
31
32   11.5.2 Decentralized networks
33   Decentralized networks usually involve information flows around the network and usually do not require
34   the construction of new purpose-built facility. There is still the basic need to decide what data and how
35   often it should be shared. There is still the possibility of bi-lateral agreements being honored so that some
36   data flows between two or more partners but for the others a more limited data flow is usual.
37
38   There are many administrative hurdles to overcome before full and free flow of information between
39   agencies and institutions will be routine. There are reasons for this. The principal one is that the
40   monitoring sites that were set up by each institution were designed for their own special purpose. There
41   was little thought given to how compatible their data sets might be with others because it was never
42   envisaged that they would be shared.
43
44   Because this proposed network involves 4 nations it is up to each of their governments to decide on the
45   extent to which they will participate in data collection and sharing, the nomination of which monitoring
46   stations and data processing centers are involved and so on.
47
48   In most countries the mandate for making forecasts on weather-related phenomena or giving warnings
49   about events such as DSS is given to the Meteorological Administration (MA). The data collected by the
50   various MA is not readily available to the modelers who are mostly researchers working in universities or
51   research institutes. If the MA were not involved in the program, it will be hard to achieve one the
52   major goal of this program -- forecasting and EW of the DSS.
53
54
55



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 1   11.6 Measures to improve the Forecasting and EW capacity through the regional network
 2
 3   At present there are several levels of data sharing by countries within the region. Most are not exclusively
 4   for DSS but the data generated by the monitoring stations is being used in various predictive models and
 5   in forecasting and early warning. There is a hierarchy with stations linked to the World Meteorological
 6   Organization (WMO) global weather monitoring network. Data is routinely collected by the
 7   meteorological authorities in each of the partner countries and transmitted in real time to the WMO node
 8   via the GTS. In addition weather satellites routinely monitor the entire globe and data may be downloaded
 9   from the satellite, either regularly or on special occasions. Japan and China have their own dedicated
10   satellites to monitor weather in the NE Asian region.
11
12   The existing monitoring stations were established by the various agencies in each country for different
13   purposes and there was no attempt made to standardize the equipment, data handling or processing. This
14   makes it more difficult to compare data sets. There is general lack of integration among the various
15   institutions in PRC where the tradition seems to be that duplication is all right and where self-sufficiency
16   in data gathering is the ideal. Data sharing has not been encouraged in the past and much archival material
17   is inaccessible (see Tables 3 and 7 in section 9 ).
18
19   Another problem is that the monitoring stations that are distributed over the source regions are under the
20   control of different Ministries, institutes, and agencies. There is little cooperation between them.
21   Data are sent regularly to the capital city for archiving; some in real time (if it aids weather forecasting)
22   but much relies on monthly reporting to the central data repository.
23
24   11.6.1 Bilateral linkages as part of the regional network
25   Bilateral agreements already exist with strong links between Japan and China, Korea and China and
26   Mongolia and Korea (see earlier reference to ADEC and ACE etc). Because many of these are at the
27   level of research agencies their purpose is limited to exchanging data and personnel and to joint research
28   projects. They lack the official status that is probably required if the regional cooperation is to achieve its
29   potential.
30
31   Governments, planners and others also want to know the impacts of any DSS event. So the monitoring
32   should include following:
33       When and where DSS event happened
34       Transportation route(s), direction and behavior of the DSS
35       Catalogue of impacts caused – including direct economic losses, impacts on human health, on
36           industry and commerce, and on communications and infrastructure
37
38   Bilateral arrangements also exist so that selected real-time data can flow between countries. Some of these
39   involve government-to-government agreements. Archival data is also exchanged through bilateral
40   agreements between research institutes in the various countries (see section 15 on data sharing)
41
42   The principal deficiency from the point of view of DSS is that the data are not principally collected for
43   that purpose. Routine meteorological observations might include data on wind and atmospheric pressure,
44   temperature, humidity, net radiation and so on but this in itself is insufficient to put into predictive models
45   on dust uplift (entrainment) and transport.
46
47   Bilateral agreements are on the increase but the MoU that set them up are rather specific as to how widely
48   the data can be distributed and the agreements regarding publication of any results are restrictive.
49   Generally these are for academic research and are not designed as tools to improve public awareness of
50   impending disasters such as DSS. The WMO network works well but has a narrow and specific purpose.
51   Because they official it is not easy for researchers to get real-time data. Often the meteorological stations
52   that are part of the WMO network are those in urban and near urban locations and not many are located in
53   remote areas that are known to be sources of DSS.
54
55



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 1   12. Organizational structure of the proposed network
 2   The ultimate objective of building a regional DSS network is to monitor and assess the DSS threat.
 3   A well-planned network would:
 4        harmonize data collection and management, analytical methods,
 5        build capacity in the regional institutions, and
 6        foster research in the use of new information technologies and space based technologies
 7        foster research mathematical modeling to enhance responsiveness to perceived threats from DSS
 8           events.
 9
10   The structure can take several forms. There are two broad categories of network and many variants of
11   each. Basically we can envisage:
12   (i) A flexible network of institutions of agencies taking part on a voluntary basis with each member’s
13   autonomy remaining intact. Such a network would rely on existing linkages among identified national
14   institutions.
15   (ii) A more formal structure that made it mandatory to report data, collected in way that was compatible
16   with the requirements of a central agency. The participants would operate in such a way that the activities
17   of the network would be delegated to the member institutions or agencies enjoying comparative
18   advantages and a proven track record to perform the jobs required now and into, the foreseeable future.
19
20   Links should be formed between institutions to avoid unnecessary competition and duplication of effort.
21   Institutions working on similar issues may arrange to specialize and to dovetail their activities to enhance
22   cost-effectiveness.
23
24   Under such a scenario there should be a Network Task Manager in each country whose primary function
25   will consist of facilitating the networking process. This includes:
26        maintaining or improving its communication infrastructure
27        overseeing its operating procedures, supervising its resources, activities and outputs
28   linking with other relevant organizations and networks internationally or within the region.
29
30   Setting up a Regional network implies agreement among the four partner countries and their various
31   institutions and agencies to share relevant data for the purpose of facilitating more accurate and reliable
32   forecasts of DSS events and the provision of early warning to reduce hazard. It also suggests the setting up
33   and maintenance of long-term monitoring for the purpose of assessing the success of mitigation efforts.
34
35   12.1 Toward the Development of Regional DSS Forecasting and Early Warning System
36
37   Given that the objective is to establish a regional DSS monitoring network in Northeast Asia and
38   implement DSS forecasting and early warning service with an aim of mitigating the losses and damages
39   caused several steps need to be followed:
40    Establishing criteria for DSS classification to facilitate the joint monitoring and forecasting carried out
41       by partner countries.
42    Establishing an operational system based on synoptic meteorology to carry out DSS forecasting and
43       early warning.
44    Developing numerical simulation in order to establish quantitative prediction methods for DSS and
45       effectively carry out DSS forecasting and early warning.
46    Improving DSS forecasting and early warning services in order to effectively prevent and mitigate
47       the losses and damage caused.
48
49    12.2 The Main Tasks for Developing Regional DSS Forecasting and Early Warning System
50   There is a need to develop guidelines for DSS classification. The establishment of criteria should be based
51   on the data what we can acquire at present. They should take into account the technique and method being
52   in used for monitoring and the long-term observational data status in regional level in NE Asia, in
53   particular the source regions of DSS. The criteria should be adaptable to the evolution of DSS monitoring




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 1   techniques and be able to meet the increasing needs of its forecasting and early warning service. The
 2   quantitative criteria will be established based on the constructing of concentration measurement network.
 3
 4   12.2.1 The methodology for DSS classification
 5   There is need for a unified classification based on wind and visibility observation. WMO has classified
 6   DSS weather into 11 categories, which could be distinguished by routine weather observation elements
 7   (wind and visibility) and they have been used in operational weather observation by its member countries.
 8   Its network covers the whole world and it is the single data source for monitoring DSS in regional level in
 9   NE Asia. Therefore one of the possible means for classifying DSS in NE Asia is based on wind and
10   visibility data. In accordance with different characteristics of DSS and the weather observation acquired,
11   DSS is classified into following 4 categories:
12              Floating dust: widespread dust in suspension not raised by wind with horizontal visibility
13              between 1 to 10 km.
14              Blowing sand: dust or sand raised by wind with horizontal visibility between 1 to 10 km.
15              Dust and Sand Storm: dust or sand raised by strong and turbulent wind with horizontal
16              visibility less than 1 km.
17              Severe Dust and Sand Storm: dust or sand raised by strong and turbulent wind with horizontal
18              visibility less than 500m.
19
20   WMO criteria should be used as a regional level classification of large scale DSS in Northeast Asia. This
21   can be used now before a new generation of DSS monitoring network based on dust concentration has
22   been established.
23
24   There is also a local classification based on dust or sand concentration. TSP and PM10 are the parameters
25   that characterize the unique qualities and concentration of dust and sand in the air. It is impossible to
26   classify the large scale DSS based on the TSP or PM10 measurements without a regional level monitoring
27   network in NE Asia, but the classification can be established in local areas based on TSP or PM10 data
28   acquired there.
29   Partner countries should use TSP as a standard parameter to classify the DSS because it characterizes the
30   different particles suspended in the air but be aware of the limitations discussed in section p (particularly
31   the lack of real time data).
32
33   12.2.2 What network configuration is best to achieve the stated purpose?
34   It is likely that any attempt to foster regional cooperation in dealing with DSS will lead to the opening of
35   channels of communication and possibly strengthening of bilateral agreements and linkages. Any network
36   has costs associated with it. Some of these relate to provision of equipment for data collection, handling,
37   analysis and storage; others to data transmission and receival. No system will be without its problems.
38   Each country has a different level of development, geographic extent and population distribution and each
39   has a different ability to pay for system upgrades and network establishment and maintenance. These
40   factors need to be considered carefully in any plan to develop a regional network for DSS monitoring and
41   early warning (See section 15).
42
43   13. Criteria for data selection
44
45   The following attributes would be ideal:
46       relevant to DSS simulation for forecasting of DSS outbreaks or transport,
47       relatively easy to collect;
48       instrumented rather than visual assessment,
49       can be transmitted in real-time,
50       can be used to build a spatial distribution of dust concentration
51       can be established with minimum training and investment
52
53   Some of these attributes may be mutually exclusive. For example, instrumented sites with real-time data
54   transmission must require investment in equipment and possible communications infrastructure.
55



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 1   To be effective a data collection system for DSS Forecasting and Early Warning depends on two key
 2   requirements:
 3         Effective timeliness: High-speed exchange of DSS monitoring data between the countries in the
 4         region and among the departments within individual country. The observational data should be
 5         transmitted to users within one hour, so that the data could be applied in producing DSS forecasting
 6         and early warning.
 7         Maintained as an operational system: The system should be kept running in a seamless, stable and
 8         reliable manner. Therefore real time monitoring of its operation and emergency response capability
 9         is required. The response time should be limited to less than 30 minutes.
10
11   Ultimately the decision about what data to collect and how is for the scientists and experts drawn from the
12   meteorological and environmental agencies within each country, supported where appropriate by scientific
13   researchers. The existing core of people in the various agencies concerned with forecasting (both long and
14   short term) and the researchers who are working on transport and global climate models would specify the
15   minimum data set required to run their models.
16
17   Due to the meteorological observation practice, WMO defines DSS mainly based on wind and visibility,
18   meteorologists in East Asia observe and forecast a DSS event mostly based on wind and compare the
19   forecasting output with visibility. The numerical DSS prediction technique is in a position to forecast the
20   DSS concentration, and there are countries that issue concentration (PM10) warning (e.g. Korea).
21   Nevertheless, due to the scarcity of regional DSS concentration observations, it is still difficult to prepare
22   such a forecast at a regional level. However, from technical and control perspectives, such a forecast will
23   meet the future warning need.
24
25   The available findings make it possible to understand the relationship between the atmospheric visibility
26   and dust concentration. In the course of constructing the Northeast Asia DSS monitoring network, one of
27   the priorities is to set up visibility meter (Transmissometers) based observation stations at regional level to
28   upgrade the visibility observation from manual to instrumental as standard. Efforts will then go into
29   research on identification of the quantitative relationship between visibility and dust concentration. Such
30   an identification may link up the present visibility benchmarked DSS monitoring network with the future
31   dust concentration monitoring network. The DSS visibility forecasting combined with the DSS
32   concentration forecasting, may constitute a groundwork for the future DSS forecasting to not only predict
33   dust concentration but also compare it with the observed concentration from the network monitoring in
34   near real-time.
35
36   Real time data is essential for some purposes and would be used by modelers and forecasters and selected
37   researchers in the Forecasting center of each country. After the DSS event has passed the archival data
38   might be used as part of the national monitoring and evaluation effort to determine the success or
39   otherwise of programs designed to prevent and control DSS.
40
41   Timely receipt of data (in real time) from a network of monitoring sites linked into the network would
42   provide modelers and forecasters in the FS centers with the necessary information to make predictions
43   about DSS paths, transit time, duration and severity as well as the geographic extent. It would also allow
44   the rolling forecasts that are generated by receipt of data from upstream monitoring sites, to be transmitted
45   back to the DSS sources areas. This would improve model validation and provide real benefit to the local
46   officials in the DSS source areas.
47
48   13.1 DSS forecasting elements
49   WMO defines DSS mainly based on wind and visibility, meteorologists in East Asia observe and forecast
50   a DSS event mostly based on wind and compare the forecasting output with visibility. The numerical DSS
51   prediction technique is in a position to forecast the DSS concentration, and there are countries that issue
52   concentration (PM10) warning (e.g. Korea). Nevertheless, due to the scarcity of regional DSS
53   concentration observations, it is still difficult to prepare such a forecast at regional level. However, from
54   technical and control perspectives, such a forecast will meet the future warning need.
55



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                                                                             Prevention and control of Dust and sand storms in North East Asia
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 1   13.2 Data required for DSS forecasting
 2   According to the GOS Manual of WMO, all necessary observations on weather events such as DSS
 3   should be conducted in a standardized manner and the information should be exchanged among WMO
 4   members whenever such phenomena occur. In Northeast Asia where DSS occurs frequently,
 5   PRC and Mongolia (Figure 13) started to conduct such meteorological observation quite early and have
 6   established corresponding data sets. China Meteorological Administration (CMA) began its
 7   meteorological observation in the middle of 1950’s and has established a long series data set (Figure 14).
 8   Based on such a data set PRC has achieved progress in the studies on characteristics and regularities of
 9   DSS.
                                                                  10
                                                                  11      Due to the joint effect of weather and surface conditions (see
                                                                  12      section x), DSS forecasting needs data and information from
                                                                  13      both aspects.
                                                                  14      13.2.1 Information about meteorological observation and
                                                                  15      analysis
                                                                  16                  Meteorological observation in the northern
                                                                  17                     hemisphere will be used to analyze the
     Figure 13 The yearly number of dusty days (horizontal visibility  18                   atmospheric circulation, which will basically
     reports) in Mongolia and its trend since 1937-1999[Natsagdorj et al.,
     2003].
                                                                                       Surface Dust Concentration [g.m-3]
                                                                                                                             180
                                                                                                                                                        Simulated Surface Dust Concentration




                                                                                                                                                                                                   Dust Storm Frequency [times]
19                         cause DSS in Northeast Asia.                                                                      160
                                                                                                                                                        Observed Dust Strom Frequency          6

20                        Detailed meteorological observational
                                                                                                                             140
21                         data in DSS source area and DSS                                                                                                                                     4

22                         affected area e.g. atmospheric                                                                    120

23                         pressure, temperature, rain, humidity,                                                            100
                                                                                                                                                                                               2
24                         visibility, wind and its 3D distribution,
                                                                                                                              80
25                        Diagnosis analysis on atmospheric
                                                                                                                              60                                                               0
26                         thermo-dynamic information based on                                                                     60   70   80    90   100 110 120 130 140 150
27                         the weather observation data.                                                                                                Julia Day
28                        Numerical weather prediction products                                                             Figure 14. Co mparisons of time series of the modeled surface
                                                                                                                             dust concentrations with the observed dust storm frequency
29                         from different meteorological centers.                                                            (based on horizontal v isibility) for six stations in China in
30                                                                                                                           spring fro m 1960 to 2003.
31        13.2.2 Geographic information and surface
32        monitoring information
33               Desert distribution and soil texture information.
34               Land use/cover change information.
35               Soil moisture
36
37   13.2.3 Dust related monitoring information
38          Atmospheric optical properties measurement including horizontal visibility (by
39            transmissometers), optical depth and size mode (by solar radiation and sun photometer), vertical
40            visibility and vertical profile (by Lidar), light scattering (by nephelometer) etc.
41          The mass concentration and size mode of dust including TSP, PM10 and dust deposition etc.
42          Satellite monitoring and retrieval data for DSS: the data can be acquired from a variety of
43            meteorological satellites (see Table 5.6 & 7 above)
44
45   It should be the goal of the DSS monitoring network in Northeast Asia to acquire these kinds of
46   monitoring data in the source area and downwind area affected by DSS. It is the key point of improving
47   the forecasting and early warning.
48
49   14. Network monitoring: national and regional
50
51   14.1 Criteria for selecting network monitoring stations




                                                                                                                                                                                                                                  46
                                                     Prevention and control of Dust and sand storms in North East Asia
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 1   The indispensable function of the network stations in the future systems for FC & EW is to provide the
 2   spatial distribution of soil dust aerosol concentrations associated with DSS event. The spatial distribution
 3   of dust aerosol is needed to validate the model output in dust aerosol concentration at the near surface
 4   derived from the forecasting & EW operational system that has been established in China Meteorological
 5   Administration, and probably is/will be established in each Meteorological Agencies in Japan, Korea, and
 6   Mongolia, respectively. In most of countries of the world, the governmental law assign all the forecasting
 7   and EW responsibilities to meteorological agencies. The indispensable function of the network stations in
 8   the future FS & EWS is to provide the spatial distribution of soil dust aerosol concentrations associated
 9   with DSS event.
10
11   The validation between observed spatial distribution of dust concentrations and the model-estimated
12   distribution is performed to satisfy the requirement of DSS FS & EWS. The Chinese DSS FS (Figure 5),
13   for instance, is configured from three components, called a) Weather Forecast Model; 2) Dust Aerosol
14   Module; 3) Data Assimilation System (Figure 1). To accurately forecasting dust concentrations by the FC
15   & EW are crucially dependent on: 1) the parameterizations of dust deflation, and as such require accurate
16   information on the geographical distributions of the deserts, their surface roughness elements, grain size,
17   soil moisture, etc. [Marticorena and Bergametti, 1995], and 2) the good prediction of dynamics physics
18   from Weather Forecast Model that still have some problems for relative longer time prediction because of
19   the limitation of understanding the atmosphere dynamics worldwide scientifically. That is why we need a
20   data assimilation component in the DSS FC to obtain accurate initial conditions to do the rolling forecast.
21   The relatively accurate dust distributions could be obtained mainly from the so called ―regional
22   monitoring and early warning network stations‖ in the Data Assimilation component, and associated
23   satellite retrieval data of aerosol distributions.
24
25   The real time data that can be used at this moment for the DSS FC & EW comes from 4 main sources: (a)
26   basic meteorological data (b) visibility measurements\readings (c) PM10 and (d) Lidar. The main need of
27   network stations is to provide the real-time data to the Data Assimilation System in Forecast System
28   center. Availability of real time data is the most important criterion for selecting network stations.
29   Other key factors in the network station are data availability and timeliness of receipt.
30
31   The spatial distribution should also be considered into the selection. Based on research and monitoring in
32   recent years, the DSS sources and depositional areas have been identified. The data from these selected
33   stations should cover the source and depositional regions, and provide spatial distributions of dust
34   concentrations in details. Some stations may be reasonable in space and geography, but those stations are
35   all in extremely under populated areas. Thus, the operation and maintenance are very difficult tasks there.
36   So, the stations should be sited where management is relatively easier.
37   Those stations already operating as could also share the task of DSS monitoring. Therefore, it is may be
38   unnecessary to set up new stations in those areas where the environmental monitoring stations are
39   operating. Some environmental monitoring stations are capable of collecting real-time data with three
40   standard indicators (Visibility, PM10 and Lidar). If the network requires more stations to be established,
41   stations could be selected from the full list of existing monitoring stations. These are in different stages of
42   development and would need upgrading.
43
44   14.2 Classifying Network monitoring stations and specifying relevant instrumentation
45   In Phase 1, the network of stations will compose 25 in PRC and 5 Mongolia plus stations in Korea and
46   Japan if needed, which could be part of a chain of monitoring station from the source areas to the
47   depositional areas. Apart from standard meteorological data, the main indicator would be VISIBILITY. A
48   target in the near-term would be to upgrade each site to an instrumented one (to do away with the
49   subjectivity associated with visual estimates).
50
51   The phased development should aim at upgrading selected monitoring sites to full capability in all of these
52   over the near- to medium-term. Selected stations might warrant Lidar installations. The selected stations
53   along the chain of stations (including those in the DSS source areas) would enjoy a high priority, at least.
54
55



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                                                               Prevention and control of Dust and sand storms in North East Asia
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 1   Table 8 Specification of relevant instrumentation in the network stations.
      Measurement                                          Method                    Estimated Price
      1) Total light extinction coefficient                Horizational Visibility   $ US 25,000        Phase 1
                                                           Transmissometers
      2) Mass concentration of particulate                 TEOM- Tapered-            $ US 26,000        Some in Phase
      matter smaller than 10 m                            Element Oscillating                          1, some in Phase
                                                           Microbalance (TEOM)                          2
      3) Lidar                                             Vertical Visibility       $ US 500,000-      Phase 3
                                                                                     1,000,000
 2
 3   Phase 2 could include adding additional monitoring stations to the network (up to about 40 in total).
 4   Mongolia would have a chance to build up its stations as part of the longer-term development under the
 5   regional Master plan. The main focus in Phase 2 is to add PM10 monitoring capability to each site in the
 6   network. Because this is available in real-time and it is more useful than TSP, although technological
 7   advances in high-speed sampling and analysis may make TSP more useful in future for forecasting and
 8   EW.
 9
10   Phase 2 would also be a time to improve Mongolian capacity in modeling and simulation, training of
11   personnel for monitoring stations, data processing and interpretation etc. The possibility of getting
12   external funding for this aspect should be explored.
13
14   Phase 3 the further development of sites to include Lidar would be a feature of this phase. Selected
15   stations along the chain of stations (including those in the DSS source areas), would be a high priority.
16
17   Setting up a regional data bank (perhaps involving Russia, DPRK, Kazakstan etc) should also be
18   considered as part of this phase. Its functions could include training, information exchange on appropriate
19   technologies etc. The overall objective would have the 4 partner countries reach similar a level in terms of
20   national capacity to monitor DSS, forecast outbreaks (onset) and predict transport routes, transit times and
21   likely duration and geographic extent.
22
23   14.3 Recommended Network Monitoring stations in the region
24
25   After consultation between the various stakeholders it was agreed to propose the following sites as the
26   base stations in the network of monitoring sites in the DSS source areas. Many of these are existing
27   stations, some require upgrade. There are some difficulties to be overcome in getting free flow of data in
28   the PRC (See box 5)
29
30   Table 9 Proposed surface-based observation sites for DSS monitoring in 1st phase in China
31
             Site                             Visibility        TSP       PM10       DSS(Lidar)        Remark
     1       Gansu-Jiuquan                    √                 √         √
     2       Gansu –Minqin                    √                 √         √          △
     3       Gansu-Dunhuang                   √                 √         √
     4       Gansu-Lanzhou                    √                 △         √
     5       Ningxia-Yinchuan                 √                 √         √
     6       Shanxi-Xian                      √                 √         √
     7       Shanxi –Yushe                    √                 √         √
     8       Hebei-Zhangbei                   √                 √         √
     9       neimeng-Erlianhaote              √                 √
     10      neimeng –Huhehaote               √                 √         √          √
     11      Neimeng-Zhurihe                  √                 √         √
     12      Liaoning-Dalian                  √                 √         √          √
     13      Liaoning-Tongliao                √                 √         √          △
     14      Liaoning-Shenyang                √                 △         √
     15      Changchun                        √                 △         √
     16      Beijing                          √                 √         √          √



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    17      Shandong-Qingdao        √             √      √
    18      Shandong-Huimin         √             √      √       △
    19      Anhui-Hefei             √                    √
    20      Xinjiang-Tachen         √                    √
    21      Yulin                   √             √
    22      Shangdianzi             √             √      √
    23      Longfengshan            √             √      √
    24      Alashanzouqi            √             √      √
    25      Suniteyouqi             √             √      √
1   Note : “√” means existing equipment ; “△” means adding needed equipment; “no-remark” means not
2   thinking about adding equipment at this time.
3
4   Table 10 Selected surface-based observations sites for DSS monitoring in 1st phase in Mongolia
5
                        DSS basic monitoring stations and their monitoring program




                                                                                     T,P,WS/D
                                                 Category


                                                               Operator




                                                                                     ,Pr, Vis




                                                                                                                             Surface condition
                                                                          Power supply




                                                                                                Soil moisture
                                                                          Visibility
                                                                              IV.
                                                               III.
                                                 II.




                                                                                                                      PM10



                                                                                                                                                 Lidar
                                                                                                                TSP
    Aimag (province)    Station name
    Gobi-Altai          Altai                    1           WMO          +      +       +      +               +     +       +
    Dornod              Choibalsan               1           WMO          +      +       +      +               +     +       +                  +
    Dornogobi           Sainshand                1           WMO          +      +       +      +               +     +       +
    Dundgobi            Mandalgobi               1           WMO          +      +       +      +                     +       +
    Umnogobi            Dalanzadgad              1           WMO          +      +       +      +               +     +       +                  +
    Uvs                 Ulaangom                 1           WMO          +      +       +      +               +     +       +
    Ulaanbaatar         Ulaanbaatar              1           WMO          +      +       +      +               +     +       +                  +
6
7




                                                                                                                                                         49
1
2




                                                                                         100E
                                                                                         100E




                                                                                                                    110E
                                     80
                                     80E




                                                           90E




                                                                                                                                            120
                                                                                                                                            120E
               50N
                                                                                                                       Choibalsan
                                                                 Ulaangom                                                      #
                                                                                                        #
                                           #                                                                Ulaanbaatar
                                            Tacheng                           # Altai
                                                                                                                                               Longfengshan
                                                                                                                                                           #
                                                                                                      #                                  Tongliao
                                                                                         Dalanzadgad   Mandalgobi       Erlianhaote                      # Changchun
                                                                                                  #                   #                          #
                                                                                                         Suniteyouqi# #
                                                                                                                   Zhurihe Zhangbei Shangdianzi
                                                                                                           Huhehaote                                  #
                                                                                                                             #         #             Shenyang
              40
              40N                                                            Dunhuang                               #
                                                                            #
                                                                                   #        Alashanzuoqi                           #
                                                                                                    #       Yulin             Beiing           #
                                                                                   Jiuquan     #      #         #                                Dalian=
                                                                                         Minqin                     Yushe
                                                                                                       Yinchuan                      #
                                                                                                                        #       Huimin
                                                                                                 #                                         #
                                                                                           Lanzhou                                           Qingdao
                                                                                                             #
                                                                                                          XiAn
                                                                                                                                Hefei
                                                                                                                                        #
              30N




               20N




                  Figure 16 Proposed surface-based observation sites in the regional monitoring and early warning network for DSS
    during 1st phase
 1   Box 5 PRC faces special problems in achieving data integration
 2   In the case of PRC there are a number of barriers to a quick resolution of the problems of free exchange of data
 3   among the principal government agencies. Each of the 4 major institutions has its own individual network and
 4   whilst they are achieving positive outcomes this approach to a national and regional problem is not so efficient or
 5   cost-effective.
 6
 7   The integrated national network in PRC has to be developed step by step. Nowadays more and more PRC
 8   governmental departments and institutions have gradually realized that we should combine different individual
 9   systems into a network of monitoring and early warning on DSS in PRC. This would be more efficient and prevent a
10   lot of duplicated work and save money. Now that there is a better basic understanding of DSS there is scope for
11   integration of the various agencies. To best capitalize on the existing technology, methodology and equipment,
12   professional staff, information-bank and observation-sites a truly national network should be set up. Government
13   should consider the following recommendations.
14
15   All observation-sites, no matter which institution\agency owns them, should collect common data or information in
16   accordance with an agreed indicator-system and technological standard. That means that data from over a hundred
17   sites in northern China would form the basis for an effective monitoring and early warning system. This would create
18   significant cost saving. Funds could become available to construct new observation-sites or upgrade existing ones (if
19   required) or to build institutional capacity, buy modern instruments and software, develop human resources, promote
20   communication systems, maintain data-banks and so on.
21
22   There is need to give a clear directive to different departments or institutions to adapt their existing network in
23   accordance with priority functions and tasks. Assign specific national professional networks. For example in PRC,
24   CMA’s network will be asked to concentrate on forecasting and early warning (hazard reduction) due to severe DSS
25   events as part of their daily weather forecasts, while SEPA would concentrate on monitoring and early warning on
26   DSS in the middle or long-term, and SFA on evaluating the disaster of stronger sandstorms and give early warning of
27   desertification-trends. The role of CAS would be on researching the formation, movement, long distance transport
28   and prevention of dust and sandstorms as a so-called self-rectifying system.
29
30   All professional networks funded by central government of PRC such as NDRC or MOF should be required to offer
31   each other on-line data in real time (where appropriate) that has been collected in accordance with agreed indicator-
32   system or standards. Financial compensation procedures need to be implemented, where appropriate, to allow cost-
33   recovery for services provided to organizations outside the PRC government system.
34
35   15. Mechanism to share the simulation results among the partner countries
36
37   15.1 Operational mechanism for data sharing for simulation modeling
38
39   Data sharing is essentially for two quite distinct purposes:
40       (i) Data collection and transmission as part of the WMO agreement. It relies on real time
41       transmission of meteorological data from various monitoring stations in each member country to
42       regional center for processing (Figure 17). Data provided in this way is the basis for weather
43       forecasts. The system works well for the various meteorological agencies for which it was designed.
44       Data transmitted in this system is generally not available to researchers who work outside the system.
45
46        (ii) Data are collected under bi-lateral agreements at purpose-built monitoring stations in some of
47        the DSS source and depositional areas. Data are transmitted to the host institutions (principally for
48        research purposes). These sites have no capacity to efficiently transfer real-time data and, because of
49        their recent establishment, have no historical baseline data. Data from these sites cannot be used for
50        forecasting and EW.
51
52   Clearly each partner country needs to develop its own network and integrate the data from the various
53   sources.
54
55   15.2 Structure of the Data Collection System
56   Two issues must be considered when designing the system. Firstly, the collection of data from the DSS
57   monitoring stations. Secondly, the transmission of data among countries within the region.
                                                                    Prevention and control of Dust and sand storms in North East Asia
                                                                                                                         RETA 6068


 1   Based on the above-mentioned options, the proposed data collection network is structured as an intra-
 2   regional data collection and processing center at regional and national levels (see Figure 17).
 3
 4   The responsibilities of these two levels are as follows:
 5   15.3 The Regional Data Center
 6          To collect the DSS observation.
 7          To collect the weather observations based on GTS.
 8          To collect other information such as surface observation, forecast and warning information
 9          To integrate and disseminate the regional DSS information to national data centers. To deliver
10             the forecasting and early warning information to the national centers
11   The regional data center should be combined with the regional meteorological data center so that
12   it is easy to get global weather data related to DSS forecast (China Meteorological Administration
13   has taken the responsibilities of regional meteorological data center).
14    The GTS is a very important basis for exchanging data in operational between countries. All the
15    information collection and transportation should be based on GTS.
16
17     15.4 The National Data Center
18           To collect observation data from domestic DSS monitoring network that includes existing
19            network such as satellite observations, visibilities etc and the future network in the project.
20           To sent the data to regional center based on the DTS (Figure 17).
21           To disseminate data to domestic users for DSS forecasting and early warning.
22            The information collection and transportation in a country wide should be based on DTS.
23
24              The content of the data collected includes three sorts:
25                Basic meteorological data exchanged by various countries through GTS.
26                Data from the DSS monitoring stations under this project.
27                DSS forecasting and early warning information of various countries.
28
29   Figure 17 A schematic to show the existing GTS linkages from the respective Meteorological agencies to the
30   regional centers (blue) and suggested linkages that would facilitate the transmission of DSS –related data via the
31   GTS network (orange).


                                                                                                                  Existing
                                                                                                                  Network

           DSS
                                                   The basic                              DTS
          Network
         Existing and
                         DTS                      Meteorological
          planning                                    Data                                                           DSS
                                                                                                                  Observation
                                                                                                                  Network in
                                                                            National Center                        planning
                    National Center
                                                                            (China, Beijing)
                        (Japan)                           GTS
                               GT                                        S
                                    S                               GT
                                                                                           DTS     DSS forecasting
                                                                                                  and early warning
                                         Regional Meteo-data Center                                system (with dust
                                                                                                  production data base)



                                             Regional DSS Data Center
                                                 National DSS Data Center
                                                      (China-CMA)
                                                                                 GT
                                             S                                        S
                                        GT
                                                                                                              DSS
           DSS
                        DTS    National Center                                  National Center   DTS        Network
          Network
         Existing and
                                   (Korea)                                        (Mongolia)                Existing and
                                                                                                             planning
          planning

32



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 1   16. Long-term endeavor and development phasing
 2
 3   There is clear need for a Phased program to facilitate and promote closer regional cooperation in dealing
 4   with the serious transboundary problem if DSS. One of the major factors is the different perception of
 5   DSS in each of the 4 partner countries. An extract from a report on a mission to Japan summarizes this
 6   (Box 6). Clearly, the interest of all countries is to see an end to the periodic DSS, regardless of how severe
 7   the impact is in the particular country.
 8   Box 6 Same Event and Different Impacts
 9
10   DSS is a major transboundary environmental concern that requires regional cooperation in Northeast Asia.
11   Discussions with officials and experts in Japan noted that DSS has brought to the countries in the region quite
12   different impacts and consequences. This difference has in turn had important implications on the technical and
13   operational approaches that have been adopted by the individual countries to address the DSS concern. The Mission
14   noted that the same DSS event observed in Japan is quite different from what is observed in its originating areas in
15   Mongolia and PRC, or in the other downstream DSS affected areas in the coastal areas in PRC and Korea. While
16   generally being recognized as a natural disaster in PRC, Mongolia, and Korea, which may result in massive
17   destruction of agriculture production and basic infrastructures, serious interruption of communication services, and
18   severe impacts on human health, DSS is mainly a matter concerning air quality and public health in Japan. Some
19   DSS phenomenon discussed in Japan can hardly be recognized without applying special monitoring instruments.
20   This difference may explain why the monitoring and forecasting of DSS remains mainly as a subject of scientific
21   research in Japan, rather than an operational public service that the governments have been asked to provide in PRC,
22   Korea, and Mongolia.
23
24    This, compounded with discrepancy in technical capacity, may also explain the different views expressed by the
25   experts from different countries on how to determine a set of core monitoring indicators for the proposed regional
26   DSS monitoring and early warning network. DSS event is tangible and relatively easy to be captured, if the
27   monitoring is conducted in a location close to its originating areas. While recognizing the importance to develop a
28   set of standardized monitoring indicators for the proposed regional DSS monitoring and early warning network, the
29   discussion in Japan appeared to highlight the need to recommend a flexible operational mechanism for the network,
30   which will allow the designated monitoring stations across the region to select, from a standard indicator set, the
31   monitoring indicators that are most relevant to their specific local circumstances. Upgrading of technical capacity
32   may gradually reduce the difference in monitoring methodology among the participating countries to certain extent.
33   It may not be desirable, however, to unify the monitoring methodology across the countries regardless their local
34   circumstances.
35   Extract of a report to ADB by Mr Fei Yue October 2003
36
37   16.1 Recommendations on sites for urgent upgrade
38   16.1.1 Mongolia
39   Mongolia contributes over 30% of the DSS events. Its infrastructure is poor and there would be
40   considerable benefit to all partner countries if monitoring and data handling was improved there.
41   Among the list of needs is the establishment of national network of DSS monitoring stations. Any new
42   stations should be fully integrated with the sites in PRC. There is clear need to strengthen the effort in
43   south Mongolia (see Box 7)
44   Box 7 The main needs to be solved in Mongolia:
45   1. Establish a national network for DSS monitoring and early warning involving all the major organizations that have
46        a stake in this issue.
47   2. Improvement and establishment of DSS monitoring network. The DSS monitoring and early warning system is
48        limited by meteorological network and weather forecasting.
49        In some meteorological stations located DSS area need to change old instruments, which measure DSS
50        indicators by new one. In Mongolia, a minimum of 3 DSS monitoring stations need to be established among
51        dust and sand sources area in the intermountain windy corridor.
52   3. In the future, there is an urgent need to develop the modeling and prediction system specially assigned for DSS.
53        The forecasting of DSS should be able to predict a DSS at least 12 hours in advance. Because of poor
54        infrastructure and communication system in rural areas there is no easy way to communicate warnings as DSS
55        events occur.
56   4. Strengthen cooperation in the areas of data-sharing, exchange of the research results , experience and technology.
57   5. Systematic training and development of human resources at all levels.




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 1   16.1.2 Suggested DSS monitoring sites in Mongolia5
 2
 3   The DSS national ground monitoring network will consist of 18 meteorological stations (Table 11) that
 4   are located in dry steppe, semi desert and desert area. Desert, semi-desert, carbonate, grayish-brown or
 5   light brown soil is a potential source of DSS because of its particle size, weak and easily breakable silt
 6   layer.
 7
 8   Site selection of the monitoring stations is based mainly on natural features of the area such as frequent
 9   sand-dust storm occurrence, geographical location (inter-mountain corridors), soil texture as well as
10   impact assessment of industrial development such as mining fields (metal and coal exploration) in the area
11   which can accelerate soil loose and accumulate fine dust materials. Therefore, the basic stations are
12   located along the dust path and windward side of the significant anthropogenic sources.
13
14   The stations are divided into 2 categories. category I is a basic station, where the monitoring program
15   includes TSP/PM10 measurement and instrumental measurement of visibility, soil moisture as part of
16   AWS. Category II are ordinary meteostations with visual observation of visibility and other
17   meteorological parameters. The 18 basic stations (Table 11) will be joined to the regional monitoring and
18   early warning network for DSS (see map, below).
19
20   Table 11 The proposed DSS national monitoring stations in Mongolia
21
     #      Aimag               Station             Soil type‖                   Station       category/monitoring
                                                                                 program
     1      Bayankhongor        Bayankhongor        K11- Light chestnut dense-   Basic*
                                                    carbonated shallow           Vs, TSP& PM10, SM, SC, MP
     2                          Jinst               GSB2 – Brown desert-         Vs-eye, SC, MP
                                                    steppe (non gypsum)
     3                          Ekhiin gol          SB1SG – Grayish-brown        Vs-eye, SC, MP
                                                    extra-arid surface-gyps
     4                          Bogd                SB2 - Grayish-brown          Vs-eye, SC, MP
                                                    desert (non gypsum)
     5      Gobi-Altai          Altai               K11                          Basic*
                                                                                 Vs, TSP& PM10, SM, SC, MP
     6                          Tooroi              Sbl+SB2                      Basic
                                                                                 Vs, TSP& PM10, SM, SC, MP
     7                          Tonkhil             SB2                          Vs-eye, SC, MP
     8                          Aj bogd             Gsb2– Brown desert-steppe    Vs-eye, SC, MP
                                                    (non gypsum)
     9                          Taliin shand        K1–Light chestnut dense-     Vs-eye, SC, MP
                                                    carbonate
     10                         Khukh morit         K11                          Vs-eye, SC, MP
     11     GobiSumber          Choir               K2–Chestnut         dense-   Basic*
                                                    carbonated                   Vs, TSP& PM10, SM, SC, MP
     12     Dornod              Choibalsan          K3- Dark chestnut dense-     Basic
                                                    carbonated                   Vs, TSP& PM10, SM, SC, MP
     13                         Matad               K3OL- Dark chestnut          Vs-eye, SC, MP
                                                    dense-carbonated
                                                    residually meadowish

     14     Dornogobi           Sain shand          SB2                          Basic*
                                                                                 Vs, TSP& PM10, SM, SC, MP


     5
      As recommended by the Mongolian consultants. Two stations in Mongolia, may be equipped with Lidar and TSP
     measurement equipment under a cooperation with SEPA of PRC. These sites are in Dalandzadgad of South Mongolia (113°
     12'E, 43°35'N) and Barrun Urt of Southeast of Mongolia (113°12' E,46°42'N).




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     15                           Zamiin uud           Sb2SN-Brown desert-steppe   Vs-eye, SC, MP
                                                       solonetzic
     16                           Khuvsugul            Sb1                         Basic
                                                                                   Vs, TSP& PM10, SM, SC, MP
     17                           Mandakh              Sb1                         Vs-eye, SC, MP
     18     Dundgobi              Mandalgobi           K1                          Basic*
                                                                                   Vs, TSP& PM10, SM, SC, MP
     19                           SaikhanOvoo          K11                         Vs-eye, SC, MP
     20                           Gurvansaikhan        K1                          Vs-eye, SC, MP
     21     Khovd                 Zereg                Sk+Sb2-                     Basic
                                                       Solonchak+Brown desert-     Vs, TSP& PM10, SM, SC, MP
                                                       steppe
     22     Khentii               Gal-shar             K2                          Vs-eye, SC, MP
     23     Ulaanbaatar           Ulaanbaatar          K31- Dark chestnut dense-   Basic*
                                                       carbonated             &    Vs, TSP& PM10, SM, SC, MP
                                                       noncarbonated shallow       Lidar
     24     Umnogobi              Dalanzadgad          Sb2                         Basic
                                                                                   Vs, TSP& PM10, SM, SC, MP
                                                                                   Lidar
     25                           Saikhan              Sb2                         Basic
                                                                                   Vs, TSP& PM10, SM, SC, MP
     26                           TsogtOvoo            Sb2                         Vs-eye, SC, MP
     27                           Gurvantes            Sb11- Brown steppified-     Vs-eye, SC, MP
                                                       desert shallow
     28                           Khanbogd             Sb11                        Basic*
                                                                                   Vs, TSP& PM10, SM, SC, MP
     29                           Manlai               Sb1                         Vs-eye, SC, MP
     30     Uvorkhangai           Arvaikheer           K2                          Basic*
                                                                                   Vs, TSP& PM10, SM, SC, MP
     31                           Bogd                 Sk+Sb2                      Vs-eye, SC, MP
     32     Uvs                   Ulaangom             Bls- Meadow-marsh saline    Basic*
                                                                                   Vs, TSP& PM10, SM, SC, MP
     33                           Khar-us              Sk- Solonchak               Vs-eye, SC, MP
     34                           Zavkhan              Sb2E-Brown desert-steppe    Vs-eye, SC, MP
                                                       with eolian deposits
     35     Sukhbaatar            BaruunUrt            K31                        Basic*
                                                                                  Vs, TSP& PM10, SM, SC, MP
     36                       Bayandelger              K2                         Vs-eye, SC, MP
     37                       Erdenetsagaan            K3                         Vs-eye, SC, MP
     38     Tuv               Maanit                   K31                        Vs-eye, SC, MP
     39                       BayanOnjuul              K31                        Vs-eye, SC, MP
     40     Zavkhan           Durvuljin                S – Sand                   Vs-eye, SC, MP
 1   ―Source – Soil map of Mongolia, 1980                             *monitoring program for medium term
 2   K1 – Light chestnut dense-carbonated
 3   K11 - Light chestnut dense-carbonated shallow
 4   K2 – Chestnut dense-carbonated
 5   K3 - Dark chestnut dense-carbonated
 6   K31 - Dark chestnut dense-carbonated & noncarbonated shallow
 7   K3OL - Dark chestnut dense-carbonated residually meadowish
 8   Sb1-Brown steppified-desert
 9   Sb11- Brown steppified-desert shallow
10   Sb2 - Brown desert-steppe (non gypsum)
11   Sb2SN- – Brown desert-steppe solonetzic
12   Sb2E- Brown desert-steppe with eolian deposits
13   SB1SG – Grayish-brown extra-arid surface-gyps
14   SB2 - Grayish-brown desert (non gypsum)
15   S – Sand
16   Sk- Solonchak meadow shoric
17   Bls- Meadow-marsh saline
18




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 1
 2
 3   Figure 18 The proposed DSS monitoring network in Mongolia
 4
 5   16.1.3 PRC
 6
 7   It proposed to establish the monitoring stations by two stages: in the first stage, the monitoring
 8   installation, communication network, and software should be upgraded at 20 stations (Table 12) so as to
 9   monitor DSS-related parameters in real-time. In the second stage, 21 new monitoring stations should be
10   set up. These 41 stations, plus the national data center, would comprise the DSS-network for PRC. The
11   national data center gathers and analyzes the real-time data collected and provides the real-time
12   information to other parties. It is proposed that the China National Center be composed of the China
13   Meteorological Agency, the State Environmental Protection Administration, and the State Forestry
14   Bureau.

15   Table 12 Proposed sites for DSS-network in first stage in P R China
                     Site             Visibility    TSP       PM10             DSS(Lidar)             Remark
      1     Gansu-Jiuquan                 √           √         √
      2     Gansu –Minqin                 √           √         √                   △
      3     Gansu-Dunhuang                √           √         √
      4     Gansu-Lanzhou                 √           △         √
      5     Ningxia-Yinchuan              √           √         √
      6     Shanxi-Xian                   √           √         √
      7     Shanxi –Yushe                 √           √         √
      8     Hebei-Zhangbei                √           √         √
      9     neimeng-Erlianhaote           √           √
      10    neimeng -Huhehaote            √           √         √                   √
      11    neimeng-Zhurihe               √           √         √
      12    Liaoning-Dalian               √           √         √                   √




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      13      Liaoning-Tongliao                   √            √          √                     △
      14      Liaoning-Shenyang                   √            △          √
      15      Changchun                           √            △          √
      16      Beijing                             √            √          √                     √
      17      Shandong-Qingdao                    √            √          √
      18      Shandong-Huimin                     √            √          √                     △
      19      Anhui-Hefei                         √                       √
      20     Xinjiang-Tachen                  √                 √
 1   Note : ―√‖ means existing equipment ; ―△‖ means adding needed equipment; ―empty cell‖ means not thinking
 2   about adding equipment in the near future
 3

 4   In the second stage, the main tasks are to install additional equipment such as Lidar at the Stage 1 sites
 5   and set up 21 new monitoring stations. In time there would be 41 DSS monitoring sites in PRC for real-
 6   time monitoring connected with the planned DSS-network. The distribution of the 21 new sites is as in
 7   Table 13.
 8   Table 13 Proposed sites for DSS-network in second stage in P R China

                                                                                                          Essential
                          Site                Visibility      TSP       PM10       DSS(Lidar)           meteorological
                                                                                                           factors
      1       Xinjiang-Hetian                      □            △         △                                      √
      2       Xinjiang-Hami                        □            △         △                                      √
      3       Xinjiang-Kashi                       □            △         △                                      √
      4       Xinjiang-Wulumuqi                    □            △         △              △                       √
      5       Xinjiang-Ruoqiang                    □            △         △                                      √
      6       Neimeng-Ejimaqi                      □            △         △              △                       √
      7       Neimeng-Xilinhaote                   □            △         △                                      √
      8       Neimeng-Chifeng                      □            △         △                                      √
      9       Neimeng-Wulanhaote                   □            △         △                                      √
      10      Neimeng-Hailaer                      □            △         △                                      √
      11      Neimeng-Bayanhaote                   □            △         △                                      √
      12      Xizang-Naqu                          □            △         △                                      √
      13      Qinghai-Waliguan                     □            △         △              △                       √
      14      Gansu-Xifeng                         □            △         △              △                       √
      15      Shanxi-Yulin                         □            △         △                                      √
      16      Jilin-Longfengshan                   □            △         △              △                       √
      17      Sichuan-Mianyang                     □            △         △                                      √
      18      Hunan-Changsha                       □            △         △                                      √
      19      Jiangsu-Nanjing                      □            △         △                                      √
      20      Zhejiang-Linan                       √            △         △                                      √
      21      Anhui-Hefei                          √            △         △                                      √
 9         Note : above table ―√‖ means existed equipment ; ―□‖ means there are human-eye but need to add equipment for
10         monitoring visibility ; ―△‖ means adding needed equipment; ―empty cell‖ means not thinking about adding equipment in the
11         near future.




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 1   16.2 A phased approach to development of regional DSS network
 2
 3   First Phase 2004-2005
 4
 5   Regional monitoring and early warning
 6
 7   Physical (infrastructure and equipment)
 8       Select sites for upgraded monitoring stations in PRC and Mongolia (20 in PRC 5 in
 9          Mongolia)
10       Upgrade monitoring sites in the source areas of PRC and Mongolia, focusing on
11          instrumented visibility data acquisition and transmission in real-time
12               o Site selection and preparation for 2 new sites in Mongolia for Lidar, TSP etc
13               o Site selection for a suitable location for MODIS receiving station in Mongolia
14
15   Institutional, policy and legal aspects
16        Identify and get agreement on the institutions/agencies to be responsible for monitoring and
17            for data sharing in PRC (see Box 5)
18        Get agreement on which agency does what (taking legal mandates into account)
19        Set up and support a national DSS focal point to enhance and improve the linkage of
20            national databases with regional and sub-regional databases applying digital and
21            communication technology;
22        Begin negotiations on MoU on data sharing and networking with the NE Asian region,
23            including standardization of monitoring and reporting methods.
24        Work out a mechanism to share the simulation output among the partner countries,
25            especially those upstream countries with poor FC capacity
26
27      Research and capacity building
28          Further develop modeling and simulation of long distance transport, forecasting and
29             early warning
30          Formalize exchange visits and training programs between partner countries
31
32   Second phase (2006-2007)
33       Upgrade selected sites in PRC and Mongolia to full PM10 or TSP capability
34       Integrate regional monitoring for source identification, impact assessment and occurrence
35          forecasting
36       Develop an integrated program to include ground surface condition monitoring
37
38      Research and capacity building
39          Further develop modeling and simulation of long distance transport, forecasting and
40             early warning
41          Formalize exchange visits and training programs between partner countries
42
43   Third Phase 2008--
44       Develop full Lidar capability at selected network sites in PRC and Mongolia
45       Formal government to government agreement on a full NE Asian DSS network
46          with agreements about maintenance, cost recovery and Quality assurance
47       Organize an international symposium on transboundary aspects of DSS aimed at facilitating
48          the exchange of ideas and experiences regarding monitoring and assessment and early
49          warning
50
51
52
53




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 1
 2   16.3 An Action program for implementing the Phased program to develop a regional DSS network
 3        in NE Asia
 4
 5   Considering that the RETA 6068 project involves four partner countries and a large number of monitoring
 6   stations, the monitoring procedure for the DSS- NE Asia network has to be easy-to-use, have low
 7   operation costs and able to provide quantitative real time data. Once agreement is reached on the structure
 8   of the network and the participating institutions are identified there is need to consider the practicalities of
 9   data transmission.
10
11   16.3.1 A realistic timetable for implementation
12   Some things could be done within 12 months. Others will take much longer. This will depend on raising
13   the funds required, either through the various national government’s budgeting processes or through the
14   raising of external funds. There are also constraints imposed by the need to proceed in an orderly fashion
15   so that the upgrading and equipping of the monitoring stations can be in step. Data acquisition, data
16   transmission, data processing, storage and retrieval, and dissemination have to be developed in ways that
17   give maximum benefit and cost effectiveness.
18
19   Speedy operationalization and quality performance of the network will depend on the level of skills the
20   national coordinators possess and the efficacy of the communications between the national coordinators
21   and the members, partners, and other stakeholders and the regional support structures including UNEP,
22   ESCAP and others. The operationalization of the network would also depend on the commitment of the
23   various country parties on the formulation of well-focused program of work. A proposed Action Plan is
24   set out below (Box 8). Some actions have a suggested time-frame, others are on-going. Some require
25   considerable re-organization, others would be relatively simple to implement. The points raised in Section
26   are relevant here (Box 5 and 7).
27
28   Box 8 Key elements of a program to implement the regional DDS network
29
30
31   a) Developing the framework for the conduct of assessment and monitoring of DSS related events (including EWS)
32   at regional, sub-regional and national levels using in combination the various systems of information technologies
33   and space-based technologies;
34   b) Supporting a national focal point to enhance and improve the linkage of national databases with regional and sub-
35   regional databases applying digital and communication technology;
36   c) Developing a regional framework for the conduct of joint or collaborative information gathering and database
37   consolidation for scientific information on DSS related matters, including desertification control
38   d) Formulating programs that will provide for analysis and interpretation of data into usable form
39   e) Encourage the use of information generated by the network and devise systems for the transfer of this information
40   to decision makers, and relevant end users (including citizens of affected areas)
41   f) Develop training and research programs to improve capacity building efforts at the national level.
42
43   Preliminary discussions based on meetings with scientists and administrators in each of the 4 partner
44   countries has formed the basis of the proposed action plan Box J.
45
46   Box 9 A tentative Action plan for implementing regional cooperation in DSS monitoring
47   Action 1. Coordination among the member countries in determining the focal institutions of the network and
48   providing assistance in establishing national DSS monitoring networks.
49   Time frame: 6-12 months
50   Activities
51   a. Develop a set of common guidelines to govern the linkages among the national participating institutions. All
52   network members should bear the responsibility for providing to the National Network Manager (NNM) their DSS
53   monitoring and assessment information, while delineating the scope to which the NNM can utilize the information. If
54   necessary, the responsibility and authority shall be confirmed by means of MoU.
55   b. Conduct a survey within each partner country to determine the types and patterns of the fields in the database to
56   define the content and format of the information to be exchanged and shared via the network. This work will lay the




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 1   groundwork for subsequently setting up the Meta databases with uniform criteria and formats for DSS monitoring
 2   and EW.
 3   c. Hold a workshop regarding the construction of DSS network technologies to get agreement on which to use and
 4   how. Agree on the common language(s) to be used.
 5    d. Hold training courses on the agreed network technologies
 6   e. Select network specialists and technicians and send them to foreign language training centers before going to other
 7   member countries.
 8   Action 2 Get preliminarily agreement on the appropriate indicator systems for northeast Asian regional DSS
 9   monitoring and assessment
10   Of the two premises on which the idea is built, one is the building of an information exchange network and the other
11   is the installation of an information service system for DSS monitoring and EW. It is therefore clear that the
12   development of a robust and comprehensive indicator system for DSS monitoring and assessment is indispensable
13   for the establishment of a regional monitoring network.
14   Time frame: 6-12 months
15   Activities
16   a. Hold 3–4 international workshops on northeast Asia’s monitoring and assessment to exchange ideas pertaining to
17   the latest achievements and dynamics in the study of DSS indicators and benchmarks
18   b. Coordinate the member countries in developing the indicator systems for DSS monitoring and assessment and the
19   EW.
20   Action 3. Strengthening the data management capacity and the network communication efficiency of the NNM
21   The enhancement of the data processing and management capabilities and the network communication capacities of
22   NNM will enable the network center to assume the task of management and coordination within the shortest possible
23   period of time.
24    Time frame: 12-18 months
25   Activities
26   The specific activities to be performed
27   a. Increasing the response speed and information handling capacity of the web servers
28   b. Expanding the data storage capacities of the database servers
29   Increasing the communication rate of connecting to the Internet and the like; and
30   d. Get authorization from the relevant authorities for the designated NNM to take charge of the network’s day-to-day
31   operation
32   Action 4. Enhancing scientific and technological cooperation and exchange
33   The implementation and accomplishment of the Regional DSS Network will inevitably promote technological
34   exchange and cooperation among the 4 network countries (and more widely).
35   Time frame: On-going
36   Activities
37   a. Organizing one Asian regional workshop with the objective of exchanging information and comparing notes;
38   workshop to be followed by a study tour of PRC and Mongolia to allow participants to visit the field monitoring
39   stations and view local conditions in the source areas at first hand;
40   b. Organize an international symposium aimed at facilitating the exchange of ideas and experiences regarding
41   monitoring and assessment and early warning
42   c. Organize a study tour to a selected country that is advanced in DSS monitoring and modeling (e.g. USA, or
43   Australia). The study tour participants to be drawn from the relevant personnel in the network.
44
45   17. Cost estimate and financing plan
46
47   The long term and continental-scale nature of the DSS problem raises questions about how such
48   undertakings should be financed and organized to ensure efficiency and continuity. A single year program
49   would be useless.
50
51   Resources must be gathered at a scale required for smooth and effective implementation of the various
52   activities and program area of the network. Whilst it is absolutely essential that the member countries
53   make available funds from their own resources to the extent possible, it is also clear that external funding
54   will have to be mobilized for undertaking the various activities of the network.
55
56   Funding will be needed. There are several sources:
57   1. Contributions in cash or kind from network members and partners
58   2. Financial assistance from bi-lateral or multi-lateral donors such as GEF
59   3. Contributions from national governments



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 1   4. Contributions from regional, sub-regional and international institutions
 2   5. Donations from private sector
 3   Funding will be crucial at the installation phase of the network as financial support during the early stages
 4   can make an important contribution to ensuring broad initial participation and survival of the network
 5   through its difficult infant periods. Specifically, financial assistance from donor countries and
 6   international agencies in the form of funding network-wide core programs or, alternatively, element-based
 7   projects. Collaboration between institutions in Asia and donor institutions will be encouraged whether in
 8   the form of formal partnership agreement or informal agreements.
 9
10   Investments in appropriate technologies, particularly for electronic information exchange and electronic
11   transfer of information will be crucial to the successful operation of the DSS Network. It is estimated that
12   the budget outlay for establishing and initially supporting the operations of the NNM and the contributing
13   monitoring stations will amount to $20 million. The cost breakdown is presented in section 14. It is
14   likely that the 4 governments of the member countries will cover the cost of network coordination staff
15   and other personnel, and contribute to the operating expenses and a certain proportion of the required
16   equipment outlay. However, a considerable proportion of the network budget will have to be sourced
17   externally and this is where donor countries and agencies will play a key role in providing financial
18   assistance. It should be noted that the budget estimate did not take into account the possible share of the
19   operating cost to be met by the participating member countries either as in-kind contribution or inputted
20   costs if the activities are spearheaded or undertaken in their respective countries.
21
22   17.1 Cost recovery options
23   In order to implement and accomplish the regional cooperation in DSS monitoring, forecasting and early
24   warning on sustainable financing mechanisms, a special fund should be raised to satisfy the operating cost
25   of the DSS network. In the working draft there are several sources of funding such as contributions from
26   network members and partners, bi-lateral or multi-lateral donors, contributions from national
27   governments, contributions from regional, sub-regional and international institutions, donations from
28   private sector etc. which can be mobilized for undertaking the various activities of the network. The
29   special fund for opeating cost also can get money from these sources.
30
31   In PRC, DSS prevention is being done by several government sectors, on the one hand , it demonstrates
32   China government give more attention and take more actions to DSS prevention, on the other hand, it
33   increases the difficulties to raise money for the operating cost of the network. Because it is difficult to ask
34   these government sectors to give enough money to meet the the operating cost. A Special fund is a good
35   way to guarantee the sustainable financing and ADB is a good candidate for the management of the
36   special fund that could take the form of Foundatiuon or Trust fund to which private sector, bilateral
37   agencies, NGOs etc contribute. It could be established off-shore and the funds adminbstered by a Board.
38
39   17. 2 Capacity building
40   Getting the structure right includes training and other capacity building measures. Capacity building
41   measures may include training, experience sharing workshops and field visits to the collection and
42   monitoring sites. While the exchange of data and ideas through networking is an important element of the
43   network, exchange visits will be crucial because there is simply no substitution for human contact.
44
45   17.2.1 Human resources
46   Training will be one of the crucial elements in view of the objective of building and enhancing
47   institutional capacities at the national and regional levels for DSS monitoring and early warning.
48   There are a variety of effective training approaches, such as providing technicians for the necessary
49   training and guidance on the spot in the participating countries and inviting trainees to the appropriate
50   facility in one of the 4 partner countries for in-depth study or exchange of ideas regarding space based
51   technologies, monitoring techniques, assessment methods modeling, and network and information
52   management technologies.
53
54
55



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 1   17.2.2 Equipment and technology for early warning and forecasting
 2   To be able to identify the weak points in the delivery of services, the regional network (however
 3   configured) should be viewed as a process of data and information flow, through data capture, acquisition,
 4   processing, storage, and packaging. These are then disseminated to end–users, policy decision makers,
 5   private sector and the general public (in the case of impending hazards such as severe DSS events). All
 6   the data, information, institutional arrangements, human resources and technology) must be integrated to
 7   facilitate an efficient flow of information. This applies at all levels of operation (local, national, regional).
 8   It is inevitable that upgrading of equipment will be involved. Hardware requirements are discussed in
 9   section 9 and 11 software needs for more precise prediction and forecasting are discussed in Annex 2.
10
11   17.3 Policy implications
12   It must be recognized that the implementation of the network will be subject to a variety of constraints. In
13   particular, resource constraints, notwithstanding the seriousness and the urgency that each of the 4
14   governments attach to the problem of DSS, will affect the speed and adequacy of the implementation of
15   the network programs. Hence, activities need to take place in phases.
16
17   Of the many elements that have bearing on the success of the network, proper design, involvement of the
18   various stakeholders and employment of cost-affordable technologies are perhaps, the most essential. To
19   be effective the Network will not be able to function without a sustained level of enthusiasm on the part of
20   the participating members and commitment from their national governments
21
22   17.4 Institutional adjustments required at national and regional levels
23   The basic question here is What is the optimal organizational structure of a network on regional
24   monitoring and early warning on prevention and control of dust and sandstorms in Northeast Asia?
25
26   This multilateral regional technical assistance project is very important but is full of great challenge to all
27   participating parties. There are at least two difficulties that we have to cope with.
28
29   Firstly, how to deal with the gaps in our present knowledge and foster cooperation between partner
30   countries e.g. PRC needs Mongolia’s data in real-time while Japan and Korea side need PRC real-time
31   data. But under the existing arrangements there are gaps and we cannot efficiently implement the long-
32   distance transport and forecasting models.
33
34   Secondly, how to deal with the investment-compensation in a fair and equitable way so that each country
35   that invested capital in setting up the monitoring sites can get recompense. Unless this problem is
36   addressed there may be a little positive activities and interest in better regional cooperation.
37
38   As for the organizational structure the main question is: who will play an essential and key role in the
39   regional network - a international organization or a country or joint executive-agency which can be in
40   charge of coordinating interesting-parties involved and raising additional funds to promote each
41   country’s communication-system and maintain ongoing-costs of the regional network?
42
43   Two steps are necessary to establish a regional network. A decentralized regional organizational structure
44   should be set up in each partner country in advance of any attempt to get full integration at the NE Asian
45   regional level
46   In this step our main tasks are on: a) launch, as soon as possible, a study in each country to conduct a
47   comprehensive assessment of current networks belong to different departments or institutions;
48   b) set up a joint planning group in each country empowered to develop a monitoring-strategy for DSS
49   (including agreement on technical standards), a draft a cooperation-framework.
50   c) agree on an acceptable compensation-mechanism whereby the real-time-data will be exchanged
51   between different interested parties. Only then will we go forward to the next step – a formal
52   organizational structure of the NE Asian regional network.
53




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 1   Without question we need to adjust institutional system or framework at both the national and regional
 2   levels. Naturally, the national level is fundamental and work should begin there on the three steps listed
 3   above. Meanwhile we have to strengthen existing bilateral or multilateral links.
 4
 5   17.5 Cooperation with the other regional or international organizations or systems
 6   One of the important obligations of the Regional DSS network and its host institution(s) is to coordinate
 7   among the partners network-building efforts, and provide specific technological assistance and guidance.
 8   Programs will be designed for promoting the role of science and technology in preventing and controlling
 9   DSS on the one hand and blending indigenous knowledge and modern science and technology on the
10   other, especially in the early warning system.
11
12   As a transboundary problem it is clear that memoranda of understanding, and other government-to-
13   government agreements could be put in place.
14
15   The launching of the proposed Regional DSS Network would provide opportunities for members of the
16   international community to put in concrete terms scientific cooperation against DSS in northeast Asia. In
17   particular, interested affected and developed country parties will be able to work more closely and
18   effectively, within the framework of the regional network, with international regional and sub-regional
19   organizations. Reference has already been made to the WMO network and to the East Asia Acid
20   Deposition Network and the contributions that each of the partner countries makes now. Opportunities
21   exist to further enhance these linkages and extend them to cooperation in the Asia-Pacific region
22   (including USA, Australia) and to Central Asia.
23
24




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 1   18. Budget estimates and costing on proposed regional DDS network
 2
 3      18.1. Mongolia
 4   As mentioned above there is no existing DSS monitoring network in Mongolia, therefore we have
 5   considered following assumptions:
 6   - all costs are approximate,
 7   - international transportation cost is assumed to be 5-15% of total equipment cost,
 8   - installation cost includes training and consulting costs.
 9
10   18.1.1.Ground monitoring cost
11
12   A/ Proposed equipment purchases for short-term program:
13
     Required items             Pieces                       Unit price                 Total price
                                                             (in thousands USD)         (in thousands USD)
     AWS                           2                         43.0                       86.0
     TSP                           5                         17.0                       85.0
     PM10                          5                         20.0                       100.0
     Visibility sensors            4                         16.0                       64.0
     Soil moisture                 14                        5.0                        70.0
     Sensors
     Drying oven & precise 2                                  8.0                       16.0
     balance
     Nephelometer                  2                          16.0                      32.0
     Total cost of equipments                                                           453.0
     Installation cost including training (we calculated as 20% of equipment cost)      90.6
     Transportation cost
                                  - International transportation (15% of total cost)    72.5
                                  - Domestic transportation (by plane, car and train)   50.0
     Construction cost for Lidar construction facility and ground monitoring station
     construction (2*100000USD + 5*30000USD)                                            350.0
     Import tax and custom clearance (20.75% of total cost of equipment)                94.0
     Total cost                                                                         1110.1
     Operating cost of 5 sites based on 10-year activity (including                     1000.0
     Administrative, Communication, Labor, Training, Maintenance, Spare
     parts etc.)
     Grand total Cost                                                                   2110.1
14
15           B/ Proposed equipment purchases for medium/long term program
16
     Required items                                 Pieces              Unit price              Total price
                                                                        (in thousands           (in thousands
                                                                        USD)                    USD)
     AWS                                              5                 43.0                    215.0
     Visibility sensors                               10                16.0                    160.0
     Soil moisture                                    20                5.0                     100.0
     Sensors
     PM10                                             20                 20.0                   400.0
     Lidar/Radar                                      2                  500.0                  1000.0
     Total cost of equipments                                                                   1875.0
     Installation cost including training (we calculated as 30% of equipment cost)              562.5
     Transportation cost
                                  - International transportation (15% of total cost)            281.3
                                  - Domestic transportation (by plane, car and train)           100.0

     Import tax and custom clearance (20.75% of total cost of equipment)                        389.1
     Grand total cost                                                                           3207.9
17



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 1   Some of the proposed sites will require continuous power supply. However, electricity supply cost is not
 2   included in the ground monitoring costing because this needs a detailed financial analysis and cost
 3   comparison.
 4
 5   18.1.2 Remote sensing cost
 6
 7   Estimated cost of equipment purchases to establish and upgrade remote sensing in Mongolia
 8
     Cost type                                                                               In thousands USD
     MODIS data receiving station                                                            500.0
     Installation cost including training (30% of station cost)                              150.0
     Transportation
                                  - International transportation (10% of MODIS cost)         50.0
                                  - Domestic transportation                                  20.0
     Import tax and custom clearance (20.75% of total cost of equipment)                     103.75
     MODIS data transaction cost for next 10 years                                           1000.0
     Total equipment cost                                                                    1823.75
     Operating cost based on 10-year activity (including Administrative,                     100.0
     Communication, Labor, Training, Maintenance, Spare parts etc.)
     Grand Remote Sensing Total Cost                                                         1923.75
 9
10
11   18.1.3 Systems development cost
12   Costs associated with setting up the networking system
13
     Cost type                                                                               In thousands USD
     Equipment cost
                                -   YSAT stations at 5 remote stations (15.0 th. USD each)   75.0
                                -   Central HUB
                                -   Message switching system                                 1000.0
                                -   Data storage system (IBM Total Storage Enterprise        500.0
                                    Storage Server Model 800)                                500.0
                                -   Internet Server (Sun Fire 4800 Server)
                                                                                             100.0
     Software cost                                                                           100.0
     Total cost of equipments                                                                2275.0
     Installation cost including training (10% of total equipment price)                     227.5
     Transportation cost
                                  - International Transportation cost (5% of total           113.75
                                       equipment cost)
                                  - Domestic Transportation cost                             30.0
     Data transaction cost based on 10 years (100.0 th. USD per year)                        1000.0
     Operating cost based on 10-year activity                                                800.0
     Grand total cost                                                                        4446.5
14
15
16   Total project cost in next 10-years to operate DSS network in Mongolia will be 11688.25 thousand USD (about
17   $12 million) but this cost does not include long-term operating cost, future equipment upgrading and
18   additional equipment purchase.
19
20   18.2 .PRC
21   18.2.1 The monitoring station cost in phase one
22   With the development of economy,enviroment protection and ecological problem have been getting more
23   and more attention in China. Much money has spent in the related projects which include R&D projects,
24   such as NWP (Numerical Weather Prediction) framework in the forecasting of DSS at the very-short and
25   short ranges, meteorological stations of CMA and environmental stations of CEPA, etc.. So in China,
26   there are several independent systems relating to the DSS forecasting and early warning. We can use these
27   systems in the phase 1 to develop the network of prevention and control of DSS in northeast Asia. That is,



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 1   we can select network monitoring stations from the existing systems which satisfy the creteria. It will be
 2   cost efective. Therefore, there are two kinds of financial questions in phase 1. One is the fair and rational
 3   compensation for Chinese contribution, the another is incremental investment in order to make the
 4   selected monitoring stations qualified for forecasting and early warning of DSS.
 5
 6   In phase 1 China will contribute 20 existing qualified stations for the forecasting and early warning of
 7   DSS in northeast Asia. In phase 1, China only can contribute the existing stations which can supply the
 8   basic meteorological data and Visibility data that we get by human eyes. PRC would have to spend
 9   more on equipment and related investment to effectively contribute to the proposed network for
10   prevention and control of DSS in northeast Asia.
11
12   18.2.2 The monitoring station cost in phase two

13   In phase 2, China proposes to contribute the stations which can supply some DSS data such as Visibility,
14   TSP, PM10, and LIDAR. So in phase 2, the main financial question is the financial costs to develop the
15   proposed stations and network.
16   In phase 2, the number of the monitoring stations will increase to 41. It means 21 new stations will
17   become the part of network. The costs associated with this are set out in Table 14.
18   Table 14 Proposed development cost in phase 2 in PRC
                Cost Type                 Pieces      Unit price (In thousands US$)       Total price (In
                                                                                         thousands US$)
                 Visibility                    19                   25.0                      475.0
                    TSP                        21                    5.0                      105.0
                   PM10                        21                 11.0-22.0                231.0-462.0
                  LIDAR                         5                   176.0                     880.0
                                    Total equipment cost                                  1691.0-1922.0
                       Transportation cost (5%of total equipmentcost)                       84.55-96.1
          Installation cost including training(10% percent of equipment cost) and          669.1-692.2
                                 Construction cost for Lidar
     Incremental operating cost of total monitoring stations for DSS based on 10-year         1500.0
                                            activity
                                          Total cost                                      3944.65-4210.3
19
20
21   The incremental investment includes:3 pieces of TSP monitoring equipment, 3 pieces of Lidar
22   measurements instruments and other investment such as human capacity building, cross-country
23   communication for data-sharing, etc. The incremental investment for phase 2 is shown in Table 15.
24
25   Table 15 Proposed investment during phase 2 in PRC
               Cost Type              pieces     Unit price (In thousands US$)            Total price (In
                                                                                         thousands US$)
                   TSP                           3                    5.0                      15.0
                       ①
                 LIDAR                           3                   176.0                    528.0
                                     Total equipment cost                                     543.0
           Installation cost including training (10% percent of equipment cost) and           354.3
                                   construction cost for Lidar
                        Transportation cost (5%of total equipment cost)                        27.15
                                      ②
         Incremental operating cost of monitoring stations for DSS based on 2-year             164.8
                                      activity (2004-2005)
                                            Total cost                                        1089.25
26   Notes:
27   ① includes two parts equipment, one is Mie-lidar, another is computer needed by Mie-lidar.
       It
28   ② Because the increment investment in equipment, the stations need more staff and related operating cost. According
29   to the yearbook 2003, the average wage of staff and workers in 2002 in China is 12422RMB(nearly equal to
30   US$1500), the average wage of staff and workers in the Geological Prospecting and Water Conservancy which




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 1   sector is similar to DSS monitoring is 12303RMB(also nearly equal to US$1500). In China, besides the wage, the
 2   employer have to pay the welfare for the staff. It is difficult for analyst to give a accurate data. The estimated welfare
 3   payment one person one year nearly equal to half of the wage.
 4
 5    According to the opinion of the Chinese team and the fair and rational principle, the financial analyst
 6   suggest: for the selected qualified stations in Phase 1, there is no incremental investment, the minimum
 7   compensation the beneficiary should pay China is the 20% of the financial cost to develop a new
 8   monitoring station. For the selected stations which need incremental investment to qualify for the network,
 9   the minimum compensation should take the total incremental investment into account.
10   18.2.3 The development cost of national data center
11    According to the suggestion of China consultant team, national data center/forecasting center is necessary
12   for the the operating of the network of prevention and control of DSS in northeast Asia.
13
14   The development cost of national data center consists of incremental telecommunication operating and
15   maintenance cost, data storage system cost, high-quality figure working station cost and software cost.
16
17   Table 16 The development cost of national data center (In thousands US$)
                       Cost Type                       Total price (In thousands US$)
       Incremental telecommunication operating                       1210
     and maintaining cost based on 2-year activity
               Data storage system cost                              180
        High-quality figure working station cost                     578
         Software cost based on 2-year activity                      337
                         Total                                       2305
18
19   18.2.4 The development cost of regional data center
20   In order to improve the DSS forecast and early warning, A regional data center/forecasting center is
21   proposed to be established in Beijing, China.
22
23   The telecommunication system development cost of the regional data center consists of
24   telecommunication equipment, Incremental telecommunication operating and maintaining cost, data
25   storage system cost and high-quality figure working station cost.
26
27   Table 17 The development cost of regional data center (In thousands US$)
                       Cost Type                       Total price (In thousands US$)
            Telecommunication equipment                              235
       Incremental telecommunication operating                       578
     and maintaining cost based on 2-year activity
               Data storage system cost                              362
        High-quality figure working station cost                     531
                         Total                                       1706
28
29   18.2.5 Remote sensing monitoring cost estimate
30
31   Ground surface conditions are important factors in DSS outbreaks. Upgrading of the remote sensing
32   capability will assist in monitoring the round surface and in detecting the geographic extent and optical
33   density of DSS events once break out occurs.
34
35   Table 18 Remote sensing monitoring cost
                                 Cost Type                                               Total price
                                                                                    (In thousands US$)
                    Large Capacity Computing Facilities                                    100.0
                    Depreciation of Computing Facilities                                   500.0
                        Equipment for data transfer                                        100.0
                         Total Cost of Equipment                                           700.0



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                Remote Sensing Image Processing Software                              100.0
                Depreciation of Image Processing Software                             200.0
                              Other software                                           30.0
               Model and method development for 10-years                      1000.0( 100.0*10)
                   Remote sensing data cost for 10-years                       200.0( 20.0*10)
      New type Remote Sensing Image Processing Software and data                      500.0
                                   cost
                   Total Cost of Software and data cost                              2030.0
                   Operating cost (10 staff for 10-years)                      400.0( 40.0*10)
                                Total Cost                                           3130.0
 1   Notes: According to the yearbook 2003, the average wage of staff and workers of Meteorology, Seismology, Survey
 2   and Mapping, Technological Supervision in 2002 in China respectively is 16130, 16021, 16868 and 16478RMB
 3   (nearly equal to US$2000).
 4
 5   18.2.6 The operating cost of the DSS network
 6   According to the tentative action plan for implementing regional cooperation in DSS monitoring drawn up
 7   by team leader, there are four actions which should be taken by the participating countries to implement
 8   the regional cooperation in DSS monitoring.
 9
10   In action 1 and 2, in the first year, money should be spent in developing a set of common standards,
11   conducting a survey, holding workshops, holding technical training courses and language training courses.
12
13   Because there are many uncertainty factors in developing a set of common standards, it is impossible to
14   give a relatively accurate cost estimate. Just the same reason, it is impossible to give a relatively accurate
15   cost estimate of conducting a survey, holding workshops, holding technical training courses and language
16   training courses.
17
18   When the network is operatiional, the cost of organizing international workshops and the costs of
19   developing and updating the standard operational mannuals will become the part of the operating and
20   maintenance cost.
21
22   18.2.7 A Financial plan for DSS network
23   Development of a sustainable financing mechanism is very important factor for DSS network. In order to
24   set up a sustainable financing mechanisms, we should differentiate between monitoring
25   equipment/equipment-related cost and operating cost, between current operating cost in each member
26   country and operating cost arising from regional cooperation. Each requires a budget line.
27
28   According to sustainable financing principle, different source of money should be supplied to each budget
29   line. Contributions from network members and partners, bi-lateral or multi-lateral donors, contributions
30   from regional, sub-regional and international institutions, donations from private sector can be used to
31   fund monitoring equipment/equipment-related investment (perhaps througha Foundation or Trust fund).
32   Contributions from national governments and donations from private sector can be used to fund current
33   operating investment. Contributions from network members and partners, bi-lateral or multi-lateral
34   donors, contributions from regional, sub-regional and international institutions, donations from private
35   sector can be used to fund operating cost arising from cooperation.
36
37
38
39
40
41
42
43
44
45
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49
50
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1




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1   APPENDIX 1
2   Table 1     Technical Assistance framework for preventing and controlling DSS in NE Asia
3

                Design Summary                   Performance Indicators/Targets                 Monitoring Mechanisms                           Assumptions and Risks

    Goal
    To reduce the frequency, severity, and     Reduced damage by introducing an           Continued monitoring of DSS and the        The frequency, severity, and damage of DSS
    damage of the transboundary                effective regional monitoring and early    damage reports                             can be reduced through well planned and
    environmental problem of dust and          warning network                            Continued monitoring and regular           coordinated public intervention
    sandstorms (DSSs) in northeast Asia        Reduced frequency and severity by          report on land degradation and             Land degradation in the DSS originating area
    through regional cooperation               arresting the land degradation in the      improvement in the originating source      is one of the major causes of increased DSSs
                                               originating source areas of DSS in the     area                                       in the region
                                               People's Republic of China (PRC) and
                                               Mongolia
    Purpose
    To promote establishment of a regional     To establish a regional cooperation        All the parties involved (i.e., the PRC,   All the four countries have the political will
    cooperation mechanism for prevention       mechanism that is supported with           Japan, Republic of Korea, and              and policy commitment to addressing DSS
    and control of DSS in northeast Asia to    operational capacity to coordinate         Mongolia and Asian Development             through regional cooperation
    encourage and facilitate coordinated       interventions on DSS and to mobilize       Bank (ADB), United Nations                 All the four international institutions will
    interventions of all the stakeholders on   support of stakeholders for combating      Convention to Combat Desertification       cooperate with due diligence
    DSS at regional level                      DSS                                        (UNCCD), United Nations
                                               To prepare a regional master plan for      Environment Program (UNEP), and
                                               combating DSS that will be supported       United Nations Economic and Social
                                               with the following:                        Commission for Asia and Pacific
                                               (i) a phased development program for       (UNESCAP)) will establish a steering
                                               establishing a regional monitoring and     committee to provide overall guidance
                                               early warning network for DSS              for project implementation
                                               (ii) an investment strategy including      All the parties will also set up three
                                               recommendations for sustainable            technical committees to provide
                                               financing mechanism and                    technical advice on specific technical
                                               identification of eight demonstration      issues during implementation of the
                                               projects                                   technical assistance (TA)
    Outputs
    An initial institutional framework for     The initial institutional framework will   All the activities of the initial          The steering committee can make decisions on
    regional cooperation on DSS                provide a forum and enabling               institutional framework will be under      policy and operational issues on behalf of the
                                               mechanism for the major DSS                close monitoring of the major              governments concerned
                                               stakeholders to coordinate their policy    stakeholders through the steering
                                               and intervention on DSS at a regional      committee
                                               level
    A regional master plan for regional        The master plan will be approved by        The master plan will be based on a         The technical committees concerned are
    cooperation on alleviating DSSs, which     the steering committee                     comprehensive assessment of existing       capable of providing technical advice and
    will be supported with, inter alia, the                                               scientific findings and be developed in    guidance
    following:                                                                            cooperation with the multiagency
                                                                                                                          Prevention and control of Dust and sand storms in North East Asia
                                                                                                                                                                               RETA 6068

            Design Summary                       Performance Indicators/Targets                Monitoring Mechanisms                           Assumptions and Risks

(i) a phased program to establish a well-                                                national working groups under the
functioning regional monitoring and early                                                guidance of the national coordination
warning network for DSS, and                                                             agencies of PRC and Mongolia. The
(ii) an investment strategy including                                                    master plan will be reviewed by the
recommendations for sustainable                                                          technical committees concerned before
financing mechanisms and identification                                                  being submitted to steering committee
of 8 demonstration projects, four in the
PRC and four in Mongolia

Activities
1. To establish
(i) an initial institutional framework for
regional cooperation on combating DSS;         The initial institutional framework and   The work program will be reviewed by       The governments will provide the consultants
(ii) a regional data bank on DSS; and          the web site for public awareness         the technical committees and approved      with access to the data and documents for their
(iii) a website for the TA as part of the      should be in operation within one         by the steering committee                  study
public awareness program.                      month of approval of the technical
2. To review and analyze                       assistance (TA).                          Consultants are requested to submit        The project secretariat should be able to
(i) existing scientific research findings on                                             weekly report to the project secretariat   provide operational and administrative support
DSS and the existing national monitoring       The detailed work program for all the     and ADB                                    to monitor and facilitate day to day operation
and forecasting systems for DSS in the         activities should be available for                                                   of the field work
region;                                        review and endorsement by the             Project secretariat will submit a
(ii) existing national program/action          technical committees concerned, and       monthly report to ADB, national
programs on DSS and the national               for review and consideration by the       coordination agencies, and the steering
experiment/demonstration projects in           steering committee at the inception       committee
PRC and Mongolia;                              meeting
(iii) best practices for alleviating DSSs;                                               Project secretariat will circulate a
(iv) initiatives from the private sector or    Findings of the review and analysis       newsletter for information
nongovernment organizations.                   should be available for timely review     dissemination and public awareness
3. To recommend a regional master plan         by the technical committees concerned     and supervision
for alleviating DSS through cooperation
at regional level; the master plan should
be supported with
(i) a phased program to establish a
regional monitoring and early warning
network for DSS, and (ii) an investment
strategy including recommendations on
sustainable financing mechanisms and
identification of 8 demonstration
projects, 4 in PRC and 4 in Mongolia




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                                                                                                     RETA 6068
 1   ANNEX 1 DSS FORECASTING AND EARLY WARNING, SIMULATION AND MODELING
 2
 3   1. DSS forecasting and early warning, simulation and modeling
 4
 5   Modern technologies include satellites that provide imagery and other digital data (see Section mm).
 6   The first earth resources satellite went into orbit in 1972. Since then many different satellites have
 7   been launched. The main characteristics of the data provided by these satellites that are relevant to
 8   EWS relate to cost-effectiveness, image quality, and frequency. Aspects include Orbit Return time
 9   (repetitive), scene area coverage, ground resolution (pixel size) and spectral resolution. There are both
10   polar-orbiting and geostationary weather satellites maintained by USA, Russia and others. Both China
11   and Japan maintain their own dedicated satellite systems that aid in tracking and mapping DSS events.
12
13    The use of advanced aerospatial technology, remote sensing and Geographical Information Systems
14   (GIS) as well as ground-based radar and Lidar has in the recent past enhanced rapid development of
15   early warning in a more visually useful and cost-effective manner.
16
17   With the increase of social-environmental and ecological implication of DSS, the requirements for
18   enhancing DSS monitoring and early warning and for reduction of damage caused by DSS have
19   significantly increased. In Northeast Asia, countries affected by DSS, such as PRC, Republic of Korea
20   and Mongolia, have conducted DSS forecasting and early warning services through their National
21   Meteorological Services. CMA in PRC initiated its forecasting service of DSS and early warning
22   service of severe DSS for the public in 2001. Korea Meteorological Administration (KMA) did the
23   same in 2002. At present the Mongolian Meteorological Service is conducting similar services for the
24   public.
25
26   1.1. DSS forecasting and early warning
27   1.1.1 Terms and definitions
28   DSS forecasting is a technical concept under which the probability of the DSS occurrence in the future
29   and the areas subject to its transport and impact are forecasted.
30   DSS early warning is a service-driven concept under which advisories are provided to the community
31   and public, and precautions taken against a potential DSS based on the DSS forecast. A DSS early
32   warning must be issued as early as possible to prepare against and mitigate such a disaster. Thus the
33   time-frame for defining DSS early warning is determined by the technical capacity for DSS
34   forecasting.
35
36   1.2 DSS forecasting technology
37   1.2.1 DSS forecasting technique based on weather forecasts
38   A DSS occurs under atypical weather conditions. The forecast of synoptic systems that have bearing
39   on a DSS is the basis of the DSS forecasting. As the definition goes, the DSS forecasting is actually
40   the forecast of weather conditions related to DSS associated with general analysis of surface source
41   and land status. The workflow includes real-time monitoring of the occurrence and transportation of
42   DSS using remote sensing and surface observations acquired, the analysis of the soil moisture and
43   vegetation coverage of underlying sources, forecasting of the atmospheric circulation and weather
44   conditions that have bearing on a DSS using various NWP products and synoptic principles.
45
46    The dynamic and statistic based approach is adopted to analyze and diagnose the characteristics of the
47   atmospheric physical parameters and its impact on the dust or sand emission, forecast such variable
48   atmospheric elements as pressure, temperature, humidity, precipitation and wind, analyze the
49   conditions of dust raising and transporting in the sources, and identify the intensity and coverage of a
50   DSS. Figure.h shows the DSS forecasting workflow. Since the present NWP technique can give a 7-
51   day forecast of atmospheric motion and synoptic impact systems, such as the impact of cold air and the
52   activity of Mongolian cyclone, as well as a forecast of meteorological elements, it is possible to extend
53   the period of validity of DSS forecasting to seven days (see Box 1).
54




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                                                                                                       RETA 6068

 1   1.2.2 Numerical DSS prediction technique
 2   The dynamic processes of an ongoing DSS include entrainment, transportation and deposition (see
 3   ADEC chart, figure 2). The DSS numerical model is a mathematical model based on such physical
 4   processes. Therefore, the model is an integrated system consisting of the atmospheric prediction
 5   model, land surface scheme and aerosol cycle including entrainment, transportation, deposition and
 6   GIS, which can simulate the amounts of surface dust emitting, transporting and sedimentation. The
 7   numerical DSS prediction is a forecasting technique concerning dust concentration. The limited area
 8   numerical DSS prediction model forecasts the occurrence and movement of a DSS for a lead-time of
 9   two days. With the availability of high-speed computers and surface data, the period of validity could
10   be extended further if the model coverage is broadened.
11
12   The numerical DSS prediction represents the direction for the future development of the DSS
13   forecasting technique. However, due to the fact that the dust erosion mechanism modeling capability
14   and the surface data are limited, the forecasting capacity of the numerical prediction model is yet to be
15   enhanced, and also due to the fact that the modeling results fail to be checked in the absence of
16   observations on dust concentration, it is still premature to apply the numerical prediction model to the
17   DSS forecasting directly. Thus, the synoptic method and the numerical prediction technique combined
18   ought to be adopted for the DSS forecasting at this stage.
19
20    1.3 DSS Forecasting Techniques
21   DSS occurrence is associated with land surface characteristics (see section f above) and weather
22   conditions. Research indicates that most of DSS is caused by appropriate atmospheric thermal
23   dynamic conditions which are associated with a typical atmospheric circulation6. The understanding of
24   such a mechanism is the basic knowledge for producing DSS forecasting. At present, the integrated
25   analysis based on synoptic meteorology and land surface characteristics is the basic methodology for
26   DSS forecasting, which is also referred to as synoptic meteorology-based DSS forecasting.
27
28   Based on the studies on DSS occurrence mechanism, in particular on the raising and transport of dust
29   and sand, the future development should be directed to the development of numerical prediction
30   models for DSS. At present the models are still at developing stage and not many models are mature
31   enough for operational prediction. In CMA the numerical prediction model for DSS was put into
32   operation in 2002. The results of DSS simulation in 2002 are given in Figure g.
33




34
     6
      See Wang Shigong et al., Progress of research on understanding sand-dust storms In: ―Global alarm: dust storm
     and sandstorms from the world’s drylands. UN 2002 pp. xx



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                                                Prevention and control of Dust and sand storms in North East Asia
                                                                                                     RETA 6068
 1            Figure g A DSS forecast simulation by CMA (PRC)
 2
 3   1.3.1 DSS forecasting and early warning period of validity
 4   As stated above, the weather forecast based DSS forecasting period of validity could be up to seven
 5   days. The limited area numerical model simulates a two-day DSS process. In terms of forecasting
 6   accuracy, the shorter the period of validity is, the more effective the observations are, and the higher
 7   the accuracy is. Therefore, the guideline governing the DSS forecasting and early warning period of
 8   validity is to provide as long as possible DSS forecasting and early warning period of validity as
 9   long as the DSS forecasting capability permits. The current DSS forecasting and early warning
10   period of validity may be classified as set out in Box q.
11
12   Box q Classification of periods of validity for DSS forecasting in PRC
13
14       — Nowcasting and Early Warning. To forecast 0 – 3 - hour DSS occurrence, intensity and
15   coverage and issue DSS early warning 3 hour ahead based on the monitoring information and the
16   analysis of changing weather.
17       — Very Short-range Forecasting and Early Warning. To forecast 3 - 12 - hour DSS occurrence,
18   intensity and coverage and issue DSS early warning 12 hour ahead based on the weather forecast and
19   the numerical DSS prediction.
20       — Short-range Forecasting and Early Warning. To forecast 24 - 48 - hour DSS occurrence,
21   intensity and coverage and issue DSS early warning 2 days ahead based on the weather forecast and
22   the numerical DSS prediction.
23       — Medium-range Forecasting. To forecast 3 – 7-day DSS occurrence trend and coverage based on
24   the medium-range weather forecast implying the DSS occurrence conditions.
25         Seasonal prediction. To predict the DSS potential trend in a season based on the seasonal
26   prediction of precipitation, temperature and atmospheric circulation pattern (more or less as referred to
27   the normal state).
28
29   1.4 DSS early warning network and advisory service
30   Based on the Northeast Asia DSS monitoring network, each national meteorological service shall set
31   up a DSS forecasting and early warning system for regular DSS forecasting operations and for national
32   DSS early warning release. Through DCS, it is possible to exchange the DSS forecasting and early
33   warning advisories. The timely transmission of DSS forecasting and early warning messages from the
34   upstream to the downstream facilitates the early preparation and issuance of the DSS forecasting and
35   early warning advisories in the downstream areas. The joint forecasting and early warning efforts help
36   with an effective DSS disaster control.
37   All countries shall work actively to keep the community and public informed of the warning advisories
38   through various media channels to prevent or mitigate the DSS impact effectively.
39   All partner countries should strive to provide timely and early warning and advisories, as appropriate,
40   to affected citizens through the various media channels so that DSS effects will be mitigated. But it is
41   matter for each country to decide on how this is to be done.
42
43   1.5 Data and software requirements to implement the monitoring and early warning
44   Data and software requirements for such a system are set out in Box d. Full costings of the various
45   monitoring and EWS are in section 14
46
47   1.6 Cooperation and Exchanges in Regional DSS Forecasting and Early Warning
48     Formalize the regular exchanges and cooperation on the Northeast Asia regional DSS forecasting
49     and early warning:
50        1. Hold a seminar or workshop on regional DSS on a regular basis.
51        2. Hold a discussion on climatic prediction of regional DSS and a forum on forecasting and
52            warning techniques and services on a yearly basis
53
54




                                                                                                              75
                                                    Prevention and control of Dust and sand storms in North East Asia
                                                                                                         RETA 6068
 1
 2   Box d Requirements From the DSS Forecasting and Early Warning Network
 3
 4
 5   1. Hardware:
 6   Data Collection System (DCS). If the DCS is to be set up on the meteorological telecommunication system
 7   (MTS), it is not necessary to consider the cost for infrastructure. But it is necessary to consider the investments
 8   for the terminal devices for data transmission and reception at each station, for the high-speed international and
 9   national network telecommunication capacity building and for the network transmission devices and
10   telecommunication consumables.
11   Data processing and storage system. To set up a mega-capacity data storage and processing system, including
12   storage equipment, data processing computer and data dissemination server, at the national DSS data center.
13   National numerical prediction system. To set up a national numerical DSS model operation system to provide
14   information products for DSS forecasting and early warning. The hardware encompasses a model operation
15   oriented high-performance computer and other relevant equipment.
16   DSS forecasting and early warning service system. It consists mainly of graphic workstations for DSS
17   forecasting.
18   2. Software
19       The R & D efforts relating to techniques of DSS forecasting and early warning include:
20   Data processing and assimilation. They include the assimilation of surface and atmospheric monitoring data in
21   different resolutions, the technique of processing DSS concentration observations and atmospheric optical
22   observations, and the technique of assimilating and analyzing visibility and DSS concentration data.
23   Numerical DSS prediction model. Application and development of model techniques and improvement of
24   models, model experiment for operational application and model verification, assessment and analysis.
25   Analysis, study and in-depth understanding of features, regularity and impact of DSS to improve its forecasting
26   and warning.
27
28
29   2. DSS simulation and modeling
30
31   Data are collected routinely from over 2000 meteorological stations throughout, PRC, Mongolia,
32   Korea and Japan. Apart from its value as part of the archive and data-base and its day to day role in
33   weather forecasting, the meteorological data also play an important part in predictive modeling.
34
35   Numerical modeling provides a systematic approach for identifying and evaluating dust emission,
36   transport, distribution and further deposition both via dry and wet removal (e.g.,10,11). These numerical
37   methods are crucially dependent on parameterizations of dust deflation, and as such require accurate
38   information on the geographical distributions of the deserts, their surface roughness elements, grain
39   size, soil moisture, etc. 12. Validation of model simulations is another key issue in these studies.
40
41   Dust aerosol uplifting occurs in a source region when the surface wind speed exceeds a threshold
42   velocity, which is a combined function of surface roughness elements, grain size, and soil moisture.
43   Soil particles that can be transported over large distances are released by saltating sand particles7. A
44   soil dust emission scheme, providing a size-dependent soil dust emission flux, and a transport scheme
45   are necessary to be coupled into a weather forecasting model or a climate model obtaining a 3D spatial
46   distribution of soil dust aerosol (see diagram Figure h)
47

     7
      Saltation occurs through dislodgement and bouncing of individual soil particles such as sand grains. As they
     bounce they dislodge other, finer, particles that can be entrained by the wind and carried to high altitudes in
     updrafts.



                                                                                                                       76
                                                                  Prevention and control of Dust and sand storms in North East Asia
                                                                                                                       RETA 6068
 1    2.1 Proposed monitoring network for DSS
 2
 3    To accurately obtain and predict the dust cycle and associated DSS, the spatial and temporal
 4    distribution of dust loading derived from model need a validation from observed data from a network
 5    of surface stations, and better associated with satellite retrieval results for distributions and vertical
 6    profiles obtaining from LIDAR. This is the observation network proposed as an outcome of this
 7    project.
     Conceptual framework for DSS simulation and forecasting
                                                                                            Observation in dust source
      Soil dust emission scheme                   validate and parameterizat ion
                                                                                            regions (Dust emission flux, dust
            (dust emission flux)                                                            loading and dust particle-size
                                                 Pa                                         distribut ion)
                                                    r am
                                                      am
            Transport scheme                            et
                                                         t   er
                                                             e
                                                                iz
                                                                 za
                                                                   t io
                                                                     i
                                                                       n

           Weather forecast or                                               Data base for dust production
                                                                             (desert distributions, soil moisture, soil
             climate model
                                                                             texture, land use/cover etc.)
Run with the observed meteorology for a specific per iod




                                                                                                                                Established
                                                    validate                 Satellite retrieval, e.g. TOMS, and
       Asian dust distribution
                                                                             vertical distribution, e. g. Lidar profiles
                                      Va
                                         lid
                                           da
                                            p
                                            pr r r


                                            atte
                                               ov ol
                                               f
                                               fo




                                                 ,




                                                                                                                                Under construction
                                                   i
                                                   id llin
                                                     e g
                                                      in fo
                                                        i

                                                          it i re
                                                             ia ec
                                                               l c as




          Forecast & Evaluation                                              Surface observation
                                                                  r
                                                                   on t
                                                                   on t
                                                                     di
                                                                     di




            (Transport, Deposition,                                          (dust loading, v isibility, TSP and PM10)
                                                                       ti
                                                                        io




           Imapcts, Climate effect)                                          in downwind regions
                                                                       n




                                             quick response
                                                                             (Mongolia, China, Korea and Japan)


 8
 9    2.2 Inter-system data processing
10    Input parameters from the Chinese deserts, Mongolia deserts and other potential source areas have
11    been obtained and verified against surface observations in PRC. The combined data sets for the desert
12    distribution/texture, land-use/roughness length, vertical flux size distribution and satellite observed soil
13    moisture provide a coherent input parameter set for the size distributed soil dust emission scheme and
14    show satisfactory results for simulations of the DSS events in recent years. Chinese scientists and their
15    colleagues have done the model simulation of the Asian dust from 1960 to 2002. Comparisons
16    between model output and network observation during the ACE-Asia Experiment (the Asian Pacific
17    Regional Aerosol Characterization Experiment) have shown that the model reproduces, with
18    reasonable accuracy, the dust emission strength and hence the soil dust concentrations in China and
19    areas downwind of the source regions15-18.
20
21    For regions downwind of the Chinese deserts, i.e. East Asia, Mid-Pacific and West North America, the
22    simulated soil dust column and dust-loading correlate reasonably well with the TOMS AI8 spatially
23    and temporally. It was found that the strength of the trans-Pacific transport of soil dust is controlled by
24    the relative strength and orientation of the major East Asian trough system9. The trans-Pacific transport
25    of soil dust and its intrusion to the North American coast show good agreement with ground and
26    aircraft observations in the vicinity of the Olympic Peninsula in USA. The simulations suggest that the
27    peak dust concentrations generally occurred below 1000 m for sites close to source regions. For

      8
          TOMS AI Total Ozone Mapping Spectrometer
      9
          The easterly airflow at high altitude



                                                                                                                                                     77
                                                 Prevention and control of Dust and sand storms in North East Asia
                                                                                                      RETA 6068
 1   downwind locations in PRC, the peak altitudes increased to a range from 1000 to 3000 m. The peak
 2   altitudes continued to increase downwind of PRC reaching 2000 – 4000 m in East Asia, 4000 – 5000
 3   m in the middle Pacific and 5000 – 7000 m on the west North American coast. Based on all this work,
 4   a NWP (Numerical Weather Prediction) framework in the forecasting of DSS at the very-short and
 5   short ranges has already been set up (see section 10) at Chinese Meteorological Administration
 6   (CMA).
 7
 8   If the proposed early warning and forecasting system (see section 13) is established, and the data on
 9   the spatial distribution of dust aerosol is available to the forecasting system in real-time comparisons
10   can be made between predicted dust distribution and observed situation. The model can be
11   progressively updated to provide a new ’initial condition’ in the rolling forecasting (6 - 48 h) . The
12   early warning & forecasting system will then be efficiently operated for prediction of DSS hazard and
13   this service can be provided to the public.
14
15   2.3 Cross country data sharing and coordination
16   All of the partner countries in RETA 6068 are in the World Meteorological Organization’s (WMO)
17   worldwide network. Selected monitoring stations in each country supply data at frequent intervals to
18   the WMO centers. Data on visibility, wind direction and strength and other parameters of relevance to
19   DSS are collected routinely as part of the WMO requirement using similar standards and indicators
20   This is an example of an ―official‖ (Government to government) data exchange mechanism. Other
21   mechanisms exist but they have a research focus, rather than a key role in any network that provides an
22   operational basis for forecasting or early warning.
23
24   The Meteorological Research Institute (MRI) has carried out the Japan-China cooperative project
25   ADEC in conjunction with Institutes of the Chinese Academy of Sciences. DSS monitoring stations
26   have been set up in the DSS source areas in western China and along the transport route of the dust
27   aerosols on their way to Japan. The fully instrumented monitoring site in the Taklamakan desert in
28   Xinjiang provide data on the DSS outbreak and entrainment in eastward air flows. Transport and
29   deposition are also analyzed. Numerical experiments using the Global Climate (dust forecast) Model
30   (GCM) have also been carried out to assess the impact of dust aerosols on global climate.
31
32   The Sino-Japan Friendship Environmental Protection Center and National Institute for Environmental
33   Studies (NIES) of the Ministry of Environment of Japan have undertaken joint research on DSS. The
34   principal focus was on DSS transport and the environmental effect of DSS aerosols originating in
35   northern China. A DSS monitoring network was established in NE China and Japan in February 2001.
36   Six stations were established along a transect from Beijing to Erenhot - the source area. Sites were at
37   Beijing, Zhangjiakou, Zhangbei, Huade, Sonid Youqi, and Erenhot over a distance of more than 1000
38   km. In addition to the sites in PRC 3 sites were monitored in Japan10.
39
40   The Korea-China Environmental Science and Technology Exchange Center was established in 1999 in
41   the National Institute of environmental Research (Seoul). The principal function is to facilitate
42   exchange of environmental information (and personnel) and the promotion of joint research. The
43   Korean Meteorological Administration (KMA) is cooperating with the China Meteorological
44   Administration (CMA) on Asian Dust forecasting. Five DSS monitoring stations will be set up (3 in
45   eastern PRC and 2 in the Asian Dust path. These stations will be operated during the DSS season from
46   February to May with the sharing of information and exchange of experts.
47
48
49
50
51
52



     10
       Mori, I. Nishikawa, M. Quan, H. Morita, M.(2002) Estimation of the concentration and chemical composition
     of kosa aerosols at their origin Atmospheric Environment 36: 4569-4575



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