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Presentation NCEP modf by jlOY3V

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									South Asian Regional Reanalysis
            (SARR)




               Ashish Routray
    National Centre for Medium Range Weather
              Forecasting (NCMRWF)
            Ministry of Earth Sciences
               Government of India
Motivation for     South      Asian     Regional
Reanalysis
Due to the direct societal impacts, interest
in Regional Hydroclimate (precipitation,
surface temperature, soil moisture, stream
flow, drought indices, etc.) is intense and
growing.
             National Action Plan on Climate Change
                       Government of India
           Prime Minister’s Council on Climate Change

           3.8.2 ……. Regional data reanalysis
           projects should be encouraged. ……..
  South Asian Regional Reanalysis (SARR)
          A Collaborative Project between
          Ministry of Earth Sciences,
            Government of India




                      and
National Oceanic and Atmospheric Administration,
           Department of Commerce,
           United States of America
Specific SARR Goals

Refinement in methods of precipitation and radiances
assimilation.

Conduct a 5-year pilot-phase reanalysis
(to test and optimize data stream organization and the geographic domain
and assimilating model choices)

Develop high-resolution SST analysis for the Indian
ocean from satellite and in-situ observations, including moorings,
drifters and Argo floats

Design techniques for assimilation of aerosols
Generate a high spatio-temporal resolution (≤25 Km, ≤3
hours) climate data set for the 1979-2009 period over the
South Asian land-ocean region.
               Responsibilities of the Parties
NOAA agrees to:

Provide MoES full access to the archived observations used in
the global reanalysis projects.

Provide technical help, training, and guidance in organization of
data streams and in the implementation of the regional reanalysis
model.

Provide training to MoES scientists in regional reanalysis
techniques and procedures during 6-8 week annual visits to
US institutions and NOAA laboratories.

Share the NCEP data processing and quality control procedures
during reanalysis project with MoES scientists.

Support travel of NOAA and US university scientists to India in
connection with SARR project activities.
MoES agrees to:
• Provide NOAA full access to all historical and current
meteorological observations as per requirement of the project over the
Indian subcontinent and Indian Ocean, including those from Indian
satellites.
•Execute the South Asian Regional Reanalysis project through
NCMRWF.
•Provide full-time modeling scientists to develop, implement, and test
numerical codes.
•Provide 4-6 full time Ph.D. scientists to design, test, and implement
various assimilation schemes in the numerical model.
•Provide high-speed mainframe computer resources for execution
of this computationally intensive project.
•Provide storage devices and skilled manpower (data management
specialists) to organize data streams, data archival, data
dissemination, and webpage design and maintenance.
•Provide continuous high-speed internet access to project scientists,
including visiting ones.
•Provide lodging and boarding for visiting US project scientists.
                      Exchange visits

• NOAA will provide training to 2-3 MoES scientists in regional
  reanalysis techniques and procedures during 6-8 week annual
  visits to the University of Maryland and NOAA's National
  Centers for Environmental Prediction (NCEP).

• NCEP will seek resources and assistance from NOAA's
  International Activities office in meeting its responsibilities.

• NOAA and MoES scientists will meet yearly to discuss the
  project's progress, and to strategize on how to best accomplish
  the project goals.

• NOAA and MoES will separately cover travel costs associated
  with exchange visits for their respective technical and scientific
  personnel.
                        Milestones


SARR IA signed in September 2008 in New Delhi


1st Annual Review by JEM held in October 2009 in New Delhi


Functional Group created at NCMRWF for SARR in November 2009


SARR Scoping Workshop held in New Delhi in February 2010


2nd Annual Review by JEM held in October 2010 in Washington DC
The SARR Project is being carried out with an objective
that the SARR Products shall be useful for
Climate Diagnostics,
Climate Variability,
Climate Change,
Model Verification/Tuning

It is expected that
The SARR project will provide an Atmosphere-Land-
Ocean surface state description where consistency
between circulation and hydroclimate components is
assured.
To achieve the goal, assimilation of rainfall, radiance,
and aerosol observations in numerical weather
prediction models shall be carried out
        SARR Project Team at NCMRWF

Sarat C. Kar        Project Management

Ashish Routray      Assimilation- Lead

Prashant Mali       Modeling- Lead




Jaganabdhu Panda    Modeling (worked for about 3 months and
                            left in September 2010)


K. Sowjanya         Assimilation (worked for about 1 year and
                            left in September 2011)
Sapna Rana          Diagnostics (worked for about 1 year and
                            left in November 2011)
Domain chosen for SARR



                         Lat: 150S-450N (286 pts)
                         Lon: 400E-1200E (332 pts)
                         Res.: 25 km (pilot phase)
                              18 km (final SARR)
                         Cen-lat: 17.50N
                         Cen-lon: 80.00E
                   SARR
NCEP
                OBSERVATION              IMD
                 DATA BANK
NCMRWF                                Countries in
                                     SARR domain
       INCOIS             Field
                ISRO
                       Experiments
                                DATA from FIELD EXPERIMENTS
               25




                                                              Paradip
               20

                                                                  DS4        TS2 (SK)
latitude (N)




               15
                                          Chennai

                                                               DS3, TS1 (SD)                                  Land Surface Processes
               10

                                                                                                C              Experiment (LASPEX)
               5

                                    BOBMEX                                                      T
               0


                                                                                                C
                    70         75             80             85             90             95
                                              longitude (E)

               Figure 1. Cruise track and time series (TS) observation positions.
                Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E.
                SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys


                                                                                                Z
                               ARMEX
                                                                                                                  C
                                                                                                                      PROWNM
                                                                                                                       B



                                                                                                STORM Programme
SARR Scoping Workshop

held in New Delhi, India (February 10-11, 2010)

9 scientists from USA and about 20 scientists from
India participated.

Analysis method and the model as
well as domain of analysis finalized.

WRF model (3.1 version) and
WRF-3DVar shall be used to carry
out SARR Pilot phase.

The Workshop recommended an
implementation strategy for
success of the SARR project.
           Work plan at NCEP
•   Training on methodology for assimilation of the
    radiance data (mainly the older period radiance
    data) using the GSI system so that a similar
    technique can be developed later for the WRF-
    3DVAR analysis system.

•   As part of the training, experiments using radiance
    data assimilation for Indian summer monsoon
    seasons (mainly for older period) using the NCEP
    GSI system and document impact assessment.

•   Familiarization with the available     diagnosis
    package for monitoring and for calculation of
    statistics of the radiance data utilized in the
    assimilation cycle.
SARR Pilot Phase Experiments
         (1999-2003)
Analysis Scheme & Model for SARR Pilot Phase

WRF 3.1 and WRF-VAR (3.1) has been chosen for SARR
Pilot phase experiments

Several modeling and assimilation experiments have
been carried out using past data.

Most of the experiments are for July 1999 using NCEP &
NCMRWF observation datasets
                     Challenging regions for obs. data

                                        Sound




Average Number of Observations per              Av. Number of TEMP observation per day
day in July 1999                                reaching particular height in July 1999

300                                             300
                                                                                          00Z   12Z
250                                             250

200                                             200
                                       00Z
                                       06Z
150                                             150
                                       12Z
                                       18Z
100                                             100

 50                                              50

  0                                              0
      Total   TEMP   WIND      PILOT                  >800hPa   800-450hPa   450-100hPa     <100hPa


                            Blocks- 42 and 43
Mean RMSE of wind components from different observations at
                   model initial time
                                      Mean RMSE of O-B and O-A for U (m/s)

                             4                      O-B          O-A


                            3.5




               U (m/s)
                             3


                            2.5
                                  sound     pilot    geoamv        airep   synop   ship
                                                      Types of Obs.



                                      Mean RMSE of O-B and O-A for V (m/s)

                            5                         O-B         O-A
                            4
                  V (m/s)




                            3

                            2

                            1

                            0
                                  sound    pilot     geoamv        airep   synop   ship
                                                          Types of Obs.
                                  SARR Test runs with NCEP & NCMRWF data

                                           Mean of RMSE of O-B for U (m/s)                                                        Mean of RMSE of O-B for V (m/s)                                                    Mean RMSE of O-B for t (k)


Mean                          6             NCMRWF           PrepBufr                                             5                   NCMRWF                   PrepBufr                              2.5               NCMRWF             PrepBufr


RMSE of                       5
                                                                                                                  4                                                                                   2
                    U (m/s)




                                                                                                          V(m/s
                              4




                                                                                                                                                                                             t (k)
OBS-FG                        3
                                                                                                                  3                                                                                  1.5


                                                                                                                  2                                                                                   1
                              2

                              1                                                                                   1                                                                                  0.5
                                   sound        pilot         airep        synop     ship                                 sound        pilot         airep            synop   ship                           sound          airep              synop   ship

                                                          Types of Obs.                                                                         Types of Obs.                                                                     Types of Obs.




                                                                                                                                    Mean RMSE of O-A for V (m/s)                                                      Mean RMSE of O-A for t (k)
                                           Mean RMSE of O-A for U (m/s)
                                                                                                      5                                                                                      1.5
                                                                                                                                           NCMRWF          PrepBufr
Mean                   6

                       5
                                               NCMRWF               PrepBufr
                                                                                                                                                                                             1.3
                                                                                                                                                                                                                       NCMRWF          PrepBufr

                                                                                                      4                                                                                      1.1
RMSE of
          U (m/s)




                       4                                                                                                                                                                     0.9
                                                                                            V (m/s)




                                                                                                                                                                                     t (k)
                                                                                                      3
OBS-ANA                3                                                                                                                                                                     0.7

                       2                                                                                                                                                                     0.5
                                                                                                      2
                                                                                                                                                                                             0.3
                       1
                                  sound       pilot         airep         synop    ship               1                                                                                      0.1
                                                                                                                      sound        pilot           airep          synop       ship                         sound          airep                synop   ship
                                                        Types of Obs.
                                                                                                                                               Types of Obs.                                                                      Types Obs.
SARR Pilot Phase Experiments

            (i) with various Physics Options
            Dynamic Downscaling using WRF

            (ii) with various Physics Options
            Assimilation using WRF & WRF-VAR

Most of the experiments are for July 1999 using NCEP &
NCMRWF observation datasets
 SARR Pilot Phase Sensitivity Experiments

All Experiments were done for July 01- 31 1999.

With Assimilation- Cyclic, Four times a day (6-hourly)

No Assimilation- only Model run Four-times a day (6-hourly).
                 (Similar to downscaling experiments)

Precipitation in July 1999 CMAP, TRMM (3B42) and IMD Observed Rain
  Precipitation from Global Reanalysis datasets for July 1999




As can be seen, the global reanalysis has failed to bring out details of
rainfall distribution over India and higher rainfall amounts are placed at
incorrect locations
            EXPERIMENTAL DESIGN
   CU schemes          PBL Schemes          SFC Schemes        Expt. Names
Kain-Fritsch (KF)                                             KF-YSU-Noah
Betts-Miller-Janjic   Yonsei University                       BMJ-YSU-Noah
      (BMJ)                (YSU)
Grell Devenyi (GD)                        Noah Land surface   GD-YSU-Noah
       KF                                                     KF-YSU-Noah
       BMJ            Mellor-Yamada-                          BMJ-YSU-Noah
                       Janjic (MYJ)
       GD                                                     GD-YSU-Noah
       KF                                                      KF-YSU-TD
       BMJ                  YSU                               BMJ-YSU-TD
       GD                                                      GD-YSU-TD
                                          Thermal Diffusion
       KF                                      (TD)            KF-MYJ-TD
       BMJ                  MYJ                               BMJ-MYJ-TD
       GD                                                      GD-MYJ-TD
SARR Pilot phase Sensitivity Experiments
 No Assimilation            With Assimilation
It has been shown that

just downscaling of coarse resolution
global reanalysis (No Assimilation runs) is
not sufficient for accurate representation of
the Indian monsoon hydroclimate.

When regional assimilation is carried out,
such representation is improved.
SARR Pilot Phase Sensitivity Experiments



                          Experiments      have   been
                          carried out using ISRO
                          derived   vegetation     data
                          instead        of      USGS
                          climatological     vegetation
                          available with the WRF
                          model.

                          Results     indicate   that
                          hydroclimate representation
                          over India is sensitive to
                          such specifications.
                           Impact of Field phase Experiments- BOBMEX data

               25


                                                                                                Bay of Bengal
               20
                                                              Paradip
                                                                                                Monsoon Experiment
                                                                  DS4        TS2 (SK)
                                                                                                (BOBMEX)
latitude (N)




               15
                                          Chennai

                                                               DS3, TS1 (SD)                    July-August 1999
               10




               5




               0
                    70         75             80             85             90             95
                                              longitude (E)

               Figure 1. Cruise track and time series (TS) observation positions.
                Period: 16 July - 30 August 1999. TS1 - 13N,87E; TS2 - 17.5N, 89E.
                SK - ORV Sagar Kanya, SD - INS Sagardhwani, DS3 & DS4 - met ocean b uoys
Impact of Field phase Experiments- BOBMEX data
                 (00Z 12 August 1999)
 Assim- Control   Assim- with BOBMEX   Difference
    Parallel Assimilation from May 2001 to Sept 2001.
    Need of Overlapping period



Pilot phase Assimilation                                U at 850hPa
with conventional data has
been completed from 1999-
2003.

Assimilation with Radiance
data and conventional data
is being carried out for the
same period.
                                                        T at 850hPa
Parallel run period is also
being extended.
Comparison between CFSR, SARR and Observation
               (1-31 July 2000)

                          OBS
                          CFSR
                          SARR
              SARR Production Runs
            Five simultaneous Streams

      Jan. 1979 - Dec. 1985            7 years
      Apr. 1985 - Dec. 1991            7 years
      Apr. 1991 - Dec. 1997            7 years
      Apr. 1997 - Dec. 2003            7 years
      Apr. 2003 - Dec. 2009            7 years

        9-month overlap for each stream

    Total 35 years of Reanalysis Computation
                 SARR Products
             Archival and Distribution

Archival Format (Reanalysis):
                IEEE (suitable for GrADS)
                NetCDF
                GRIB2

Archival Format (Observed data):
                ASCII (GTS)
                PrepBUFR
                little-R
                Original format of data
Archival online/nearline disk, Tapes
Available to Partner Organizations: Immediately
                             Aug    Dec    Apr    Aug    Dec    Apr    Aug    Dec    Apr    Aug
Tasks                        2010   2010   2011   2011   2011   2012   2012   2012   2013   2013


Pilot phase reanalysis
production (1999-2003)
Evaluation of pilot phase
reanalysis data
Refinement of assimilation
techniques
Collection of data from
countries in SARR domain
Level-I SARR Production
for 1979-2009 period
Evaluation of Level-I
reanalysis data
Final SARR Production for
1979-2009 period
Reanalysis data- public
                SARR – What next?

SARR -II

After the successful completion of SARR’s
present project, We propose to carryout SARR-60

SARR-60 From 1950 to 2009 at 9 km resolution
        Regional Ocean-Atmosphere coupling
        - shall be the comprehensive dataset for
        climate studies in South Asia.
IMPACT of BACKGROUND ERRORS (BE)
           & ASSIMILATION
                 Numerical Experiments
•   The objective of the study is to evaluate the impact of the different
    back ground errors (Global and Regional) towards simulation of
    four Monsoon Depressions (MDs) over Indian region during SARR
    pilot phase period.
                         •   27-29 July 1999 (Case-1)
                         •   17-18 June 1999 (Case-2)
                         •   11-12 June 1999 (Case-3)
                         •   6-8 August 1999 (Case-4)


•   For this purpose three numerical experiments are carried with
    WRF-3DVAR as follows:
            1)   CNTL:               Without data assimilation using NCEP re-
                                     analyses as IC and BC.
            2)   BG-3DV:             Data assimilation using NCEP global
                                     Background Error (BE).
            3)   BR-3DV:             Data assimilation using own calculated BE over
                                     SARR region.

•   The additional observations viz. SYNOP, SHIP, TEMP, BUOYS,
    PILOT, GEOMV, AIREP etc. are used to improve the model initial
    condition derived from coarse resolution large scale global
    analysis.
a)                      Mean RMSE of O-A for U (m/s)                                             b)                              Mean RMSE of O-A for V (m/s)
           6                                                                                                 6
                               BG-3DV    BR-3DV
           5                                                                                                 5                      BG-3DV     BR-3DV
           4
U (m/s))




                                                                                                             4




                                                                                                  V (m/s))
           3
                                                                                                             3
           2
                                                                                                             2
           1
                                                                                                             1
           0
                                                                                                             0
               Sound   Synop    Geoamv   Airep                       Pilot   Metar   Ships
                                                                                                                 Sound      Synop     Geoamv   Airep    Pilot   Metar   Ships
                                    Types of OBS                                                                                          Types of OBS


                                                 c)                            Mean RMSE of O-A for Temperature (k)
                                                                       2                       BG-3DV              BR-3DV
                                                 Temperature (k)))




                                                                     1.5


                                                                       1


                                                                     0.5


                                                                       0
                                                                             Sound     Synop          Airep              Metar        Ships

                                                                                               Types of OBS




           Mean RMSE from BR-3DV and BG-3DV of O-A for a) U (m/s), b) V
                              (m/s) and c) T (K).
                     NCEP ANA           BG-3DV ANA             BR-3DV ANA
   Case-1
OBS: 21.0/89.0
CNTL:21.8/89.8
BG-3DV:21.6/88.8
BR-3DV:20.8/89.5




   Case-2          NCEP ANA             BG-3DV ANA               BR-3DV ANA


OBS:18.5/86.0
CNTL:18.5/87.0
BG-3DV:18.9/87.1
BR-3DV:19.2/86.5




                       Model Initial time wind fields at 850 hPa and MSLP
                    Case-1                                                           Case-2




                                   Track Error                                                     Track Errors

              900                                                              700          CNTL
              800         CNTL                                                 600          BG-3DV


                                                                Errors (kms)
              700         BG-3DV
Errors (km)




                                                                               500          BR-3DV
              600
              500         BR-3DV                                               400
              400                                                              300
              300                                                              200
              200
                                                                               100
              100
                0                                                                0
                      0      12           24          36   48                           0                  12          24

                                     Forecast hours                                                   Forecast hours
                Case-3                                                Case-4




                          Track Eorrors (km)                                         Track Errors (km)
                         CNTL                                                 CNTL
              250        BG-3DV                                     400
                                                                              BG-3DV
Errors (km)




                                                      Errors (km)
              200        BR-3DV                                     300       BR-3DV
              150
                                                                    200
              100
              50                                                    100

               0                                                     0
                     0                 12        24                       0           12         24       36   48
                                  Forecast hrs                                             Forecast hrs
  Spatial RMSE (mm) and Correlation Co-efficient (CC) of rainfall over the
            area (Lat=150-250N; Lon=750-900E) for all cases.

    Cases                    RMSE                          CC

                 CNTL     BG-3DV    BR-3DV   CNTL       BG-3DV     BR-3DV

    Case-1      29. 62    26. 24    22.15    0.23       0.36       0.52
(27-29 July 99)

    Case-2      24. 32    22. 31    18. 38   0.21       0.33       0.46
(17-18 June 99)

    Case-3       26. 93   22. 54    18. 68   0.26       0.35       0.46
(11-12 Jun 99)

    Case-4       40. 59   34. 25    29. 41   0.33       0.46       0.52
 (6-8 Aug 99)

    Mean         30. 37   26. 34    22. 16   0.26       0.38       0.49
                       CNTL                BG-3DV           BR-3DV
                 400   CNTL vs. BG         CNTL vs. BR      BG vs BR
                                                                       60




                                                                            Skill of Expts.(%)
Mean VDEs(kms)



                 300
                                                                       40
                 200
                                                                       20
                 100

                   0                                                   0
                        00                   12              24
                                     Forecast hours (UTC)



Mean VDEs (km) and gain skill of experiments
Impact of Radiance data
Temperature (oC) at
    850 hPa                    GTS   GTS+Rad




                  Diff. (Rad-GTS)
Wind (m/s) at 850 hPa                  GTS+Rad
                                 GTS




                    Diff. (Rad-GTS)
Wind (m/s) at 500 hPa                     GTS+Rad
                                   GTS




                        Diff. (Rad-GTS)
Rainfall Climatology




         These are accumulated 6-hrly Rainfall from the
         models used for Reanalysis. Every 6-hour,
         observed data are inserted into the data
         Assimilation systems, and analyses are carried
         out. Assumption is that models are good enough
         for at least 6 hour.
These studies show there are large uncertainties in the
Global Reanalysis data over our Region.

Model Resolution? Data Quality/Quantity?

We need to carry out a Systematic Regional Reanalysis
for our Region to have a consistent Hydro-climate
dataset.

The Global reanalysis data are utilized for studying
climate change and to develop several Application
models.

Therefore, we should provide the users with a good
quality data set for our Region.
                                                • A large part of tropical forecast
Response of the Analysis Increments to
                                                errors can be represented by
  a single Temperature observation 10K          equatorial waves.
                                         Global •These modes effectively reduce
                                         BE
                                                the mass/wind coupling at the
                                                equator.
                                                • Daley (1996) has noted that
                                         Reg.
                                                equatorial error covariance is
                                         BE     weaker than higher latitude and
                                                similar to that obtained by
                                                equatorial beta plane theory.
                                                • By suppressing the erroneous
 a single u-wind observation 1 m/s              tropical wind-height coupling,
                                         Global Daley did not find the covariance
                                         BE
                                                pattern to the south of central
                                                latitude in the tropical domain.
                                                •In our study, we find that for the
                                         Reg.
                                                BE statistics, the effect of a
                                         BE     single wind observation is
                                                consistent with theoretically
                                                derived wind correlations for
                                                non-divergent flow.

								
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