Feb TexMex Dust Storm Analysis

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					Feb TexMex Dust Storm
       Analysis
Satellite
            250m image
•   The National Polar-orbiting Operational Environmental Satellite System (NPOESS) represents this
    country’s next generation of polar-orbiting environmental satellite.
•   These projects involve improved tactical communications - direct broadcast field terminals, data mining
    techniques for large heterogeneous data bases, data retrieval algorithm development, data assimilation
    for nowcasting applications, combat simulations quantifying the value of data to the manager exploring
    other remote sensing technologies to augment NPOESS and.
•   NPOESS Preparatory Project (NPP) to launch in 2006, will provide a bridge from NASA’s EOS research
    missions (Terra, Aqua, and Aura) to the operational NPOESS mission in the years that follow.
•   America’s future (2012 period) geostationary satellite series, GOES-R, is expected to be a geostationary
    constellation whose major meteorological observing instruments are an Advanced Baseline Imager (ABI)
    with up to 16 channels, and a Hyperspectral Environmental Suite (HES) that is comprised of a
    hyperspectral imager operating in the 0.4 to 1 micron range (HES-I) and an atmospheric sounder
    operating across the 4-15 micron portion of the spectrum (HES-II). That instrument is the GOES-R HES-
    I with a spatial resolution on the order of 100 to 150 meters operating at 10 nanometer spectral
    resolution across the 0.4 to 1.0 micron range across a domain of 100x100 kilometers and capable of
    being refreshed at 5 to 10 times per minute

•   This report documents a significant dust storm outbreak that occurred on 3 March 2003 over the Gulf of
    Oman. The case study demonstrates the effective synergy of the AOD product together with various
    other satellite-derived products on the NRL Satellite Focus page and the Navy Aerosol Analysis and
    Prediction System (NAAPS) to characterize visibility conditions over data sparse/data denied regions.
    Nearby synoptic surface reports serve as validation to the satellite and model -derived products
    presented herein. This report also describes the limitations and shortcomings of the current AOD
    product arising from sun glint, clouds and water turbidity contamination factors.
•   Presently, the Naval Research Laboratory’s global and regional dust models (NAAPS and COAMPS TM
    Dust) use the USGS land use characteristic dataset to determine dust emission areas. Since its
    compilation a decade ago, two major weaknesses in the USGS land use characteristic dataset have
    become apparent. 1. The land uses describing arid and semi-arid regions in Asia and Southwest Asia
    have quickly become outdated. To update and to improve the USGS dataset, we are using GIS-like
    software named ENVI (Environment for Visualizing Images), 1 km National Geophysical Data Center
    (NGDC) global topographical data, satellite imagery, maps, atlases, and recently released governmental
    reports.
•   The science behind the Air Force Weather Agency’s (AFWA) Dust Transport Application (DTA) is
    discussed and the results of an extensive verification of DTA over Africa, central and southwest Asia are
    presented. DTA ingests AFWA MM5 45km resolution surface wind data, which is used to calculate the
    surface dust flux based on wind threshold velocity. There are differing threshold velocities based upon
    the dust particles’ diameter, air and particle density, and soil moisture. DTA also accounts for the
    vertical transport of dust through the calculation of horizontal divergence and a second parameter that
    calculates vertical diffusion. In addition, DTA uses a dust source region database that was developed on
    the basis of land use, topography, the use of Advanced Very High Resolution Radiometer (AVHRR), and
    Total Ozone Mapping Spectrometer (TOMS) data.
•   A dust aerosol model has been developed and fully embedded in the Navy’s COAMPSTM as an on-line
    module of the prediction system, using the exact meteorological fields at each time step and each grid
    point of all nests. COAMPSTM is being applied to the experimental dust forecasts for Southwest Asia
    including Iraq and Persian Gulf in Spring 2003, the season of high frequency of sandstorms in the
    region. The model is run twice a day at 00Z and 12Z to produce 3-day forecasts at 9, 27 and 81-km grid
    resolutions.
•    SATELLITE FOCUS: A DYNAMIC, NEAR REAL-TIME SATELLITE RESOURCE FOR THE DoD. (NRL) in Monterey accelerated the
     development and transition to operations of a new web-based satellite imagery interface. The philosophy of the “Satellite
     Focus” web page is sector-centric; a wide variety of value-added products populate the website in near real-time over co-
     registered domains. This provides one stop shopping for the analyst, thereby mitigating the often -burdensome task of
     searching for necessary information across a myriad of independent resources of variable coverage, capability, quality, and
     timeliness. A completely dynamic tool, the interface evolves with the introduction of new sectors and products. Intelligent
     architecture and site navigation, customizable animation, image mosaics, satellite overpass prediction and on -line product
     tutorials support cutting-edge satellite multi-spectral and model-fusion products developed by NRL Satellite Meteorological
     Applications Section scientists using a full complement of polar/geostationary satellites and NWP fields. Highlighted among
     these products are the high-resolution multi-spectral applications available from the Moderate Resolution Imaging
     Spectroradiometer (MODIS), a telemetry received in near real-time via special arrangement between NOAA, NASA, and DoD
     agencies in direct support of the War on Terrorism. A mirror website transitioned to Fleet Numerical Meteorology and
     Oceanography Center has made Satellite Focus available upon Secure Internet bandwidth and thereby more readily
     accessible to assets in theater. Constructive feedback from a wide variety of operational users during OEF and OIF has
     helped to further develop and optimize this resource. Constructive feedback from a wide variety of operational users during
     OEF and OIF has helped to further develop and optimize this resource.
•
The FNMOC Dust Discussion. Dust event forecasting is an emerging, but still immature science. With the onset of war in Iraq,
     forecasting dust has become an important issue, and forecasters in theater have been doing their best to forecast dust
     effects on operations for pilots, ground forces, and ships at sea. As forces move further inland, dust events present both a
     problem and an opportunity for effective deployment of U.S. forces. Toward this end, FNMOC has taken a two -pronged
     approach by 1) upgrading its array of satellite and model dust products, and 2) reorganizing its operational watch team to
     focus on dust analysis and forecasting. FNMOC began to make use of the NRL/MRY Aerosol Group's Navy Atmospheric
     Aerosol Prediction System (NAAPS) products prior to formal transition to operations. At NRL, the Coupled
     Ocean/Atmosphere Mesoscale Prediction System (COAMPS) was enhanced to provide aerosol prediction for Southwest Asia,
     and preliminary model aerosol output from COAMPS was made available for evaluation starting in March of 2003
An important aspect of FNMOC’s new strategy is to increase situational awareness and interaction with forward deployed
     forecasters who directly support the warfighter. To accomplish this objective, the Operations Department watch standers’
     duties were restructured to include a daily analysis of the dust products available on the Satellite Focus and NAAPS Web
     pages. This daily analysis was termed the “Dust Discussion”. The procedures for this analysis and the content of the Dust
     Discussion were developed by a group of watch standers, scientists and forecasters from FNMOC and NRL, who meet on a
     weekly basis to provide guidance, review results, and modify procedures or content as necessary. The watch standers have
     undergone training to learn how to forecast dust events. Training has included analysis of satellite imagery, basic dust sto rm
     physics, forecasting tips, and resource utilization topics.
•   FIRES: Unlike aerosol species such as dust and smoke who’s source functions can be determined
    through dynamical fields, most fires are anthropogenic in nature and hence emissions vary considerably
    from day to day. To support field operations that rely on EO systems, propagation models need to be
    able to quickly adapt to new fires. The smoke component of the Navy Aerosol Analysis and Prediction
    System (NAAPS) utilizes real time fire detection algorithms from geostationary satellites with the
    NOAA/NESDIS Automated Biomass Burning Algorithm (ABBA) and from MODIS with the University of
    Maryland RapidFire and NRL fire hotspot algorithms. We also discuss and contrast the physical optical
    properties of biomass and oil fire smokes and how they relate to light extinction in visible and IR
    wavelengths.

•   2D-4D grid data distribution system and its use in support of tactical operations. The data have been used
    in secondary modeling systems (surf, EM propagation, and chemical dispersion forecasts), planning tools
    for flight and landing missions (JMPS, Brandes Associates), and for display on a common operational
    desktop (WebCOP/XiS, Polexis). We store NOGAPS, COAMPS, WW3, SWAN and other model grids. Other parts of
    Metcast store derived data (ship routes, surf forecasts). The built-in fine-grained access control allows
    the system to be used for coalition support and joint operations.
•   The system incorporates the Grid DataBlade (Barrodale Computing Services) —stores tiles of scalar and
    vector grids arranged in time and the vertical dimension. The DataBlade can compute a subgrid, select a
    vertical post, re-project and interpolate in any dimension. Because these computations are performed
    within the database engine, they are highly efficient. A flexible query system lets the user select a 1D-
    4D (sub)grid based on a model, geographical region, valid time and other criteria. The user can also
    request a desired interpolation mode or remapping, e.g. from a Lambert-Conformal projection to spherical
    coordinates. The data distribution system is reflective and can describe, in various levels of detail,
    which gridded data are potentially or currently available.
           Project isGoal and Objective on:
     The goal of the project to provide technical support to EPA & RPOs


 Estimation of Natural Visibility Conditions over the
                          US

                             Tasks and Approach:

1.    Conceptual Evaluation of Natural PM and Visibility Conditions
      •   Establish Virtual Workgroup with representatives from EPA, RPOs,
          scientific community

2.    Quantitative Estimation of Recent Regional Natural Contribution
      Statistics
      •   Conduct Data Analysis for estimating natural contributions (1995+, surf. and
          satellite obs)
Task 1: Conceptual Evaluation of Natural PM
          and Visibility Conditions
 • Establishing the main natural source types, e.g.
    – Windblown dust (local and distant)
    – Biomass smoke (forest, grass and other uncontrolled fires, local and
      distant)
    – Biogenic emissions (trees, marshes, oceans)
    – Sea salt


 • Physico-chemical properties of natural aerosols
    – Size distribution
    – Chemical composition
    – Optical properties


 • Evaluate suitable metrics for statistically describing
   natural conditions
    – Relevant aerosol components (e.g. SO4, NO3, OC, EC, Dust)
• Atmospheric aerosol system has three extra dimensions (red),
                       Background
  compared to gases (blue):
   –   Spatial dimensions (X, Y, Z)
   –   Temporal Dimensions (T)
   –   Particle size (D)
   –   Particle Composition ( C )
   –   Particle Shape (S)

• Bad news: The mere characterization of the 7D aerosol system
  is a challenge
   –   Spatially dense network -X, Y, Z(??)
   –   Continuous monitoring (T)
   –   Size segregated sampling (D)
   –   Speciated analysis ( C )
   –   Shape (??)

• Good news: The aerosol system is self-describing.
   – Once the aerosol is characterized (Speciated monitoring) and
     multidimensional aerosol data are organized, (see RPO VIEWS effort),
          Aerosols: Many Dimensions
• Compared to gases (X, Y, Z, T), the aerosol system
  has four extra dimensions(D, C, F, M).
   –   Spatial dimensions X, Y          Satellites, dense networks
   –   Height             Z             Lidar, soundings
   –   Time               T             Continuous monitoring
   –   Particle size      D             Size-segregated sampling
   –   Particle Composition             C         Speciated analysis
   –   Particle Shape/Form              F         Microscopy
   –   Ext/Internal Mixture             M         Microscopy
 •Bad News:The mere characterization requires many tools.
 Some tools sample a small subset of the xDim aerosol data space
 These need extrapolation, e.g. single particle analysis
 Other tools get integral measures of several dimensions
 These require de-convolution of the integral, e.g. satellite sensors
                                                                        Satellite-Integral
   Aerosols: Opportunity and Challenge

• Good news: The aerosol system is self-describing.
   – Once the aerosol is characterized (size-composition, shape) and
   – Spatio-temporal pattern are established,
   – => The aerosol system describes much of its history through the
     properties and pattern, e.g source type (dust, smoke, haze),
     formation mechanisms, atmospheric interactions. and
     transformations.
   – The ‘aerosol’ dimensions (D, C, F, M) are most useful for
     establishing the sources and effects, including some of the
     processes.
   – The Source of can be considered an additional, ‘derived’ aerosol
     dimension.

• Analysts challenge:        Deciphering the handwriting contained in
  the data
   – Chemical fingerprinting/source apportionment
   – Meteorological transport analysis
   – Multidimensional data extrapolation, de-convolution and fusion
       Local, Sahara and Gobi Dust0.8

                                                        Sahara     SW US
                                  0.7

             over N. America      0.6

                                  0.5


                                  0.4

                                  0.3

                                  0.2

                                  0.1

                                   0
                                        Al/Si   Fe/Si      Ca/Si    K/Si   Ti/Si




• The dust over N. America originates from local sources
  as well as from the Sahara and Gobi Deserts

• Each dust source region has distinct chemical signature in
  the crustal elements.
Seasonal and Secular Trends of Sahara
          Dust over the US




                                     Seasonally, dust peaks
 Regional Sahara Dust events occur     sharply in July when
      several times each summer          the Sahara plume
                                          swings into the
                                            Caribbean.
              (Poirot, 2003)
Dirty dust composition based on Positive Matrix
              Factorization, PMF
                            At Brigantine, NJ, dust
                              composition is enriched
                              by SO4 (30% dirty dust
                              mass) and NO3 (8%)


                                   ‘Dirty’ dust and salt
                                   composition
Direction of Dust Origin at 5
      IMPROVE Sites
             High ‘dust’ concentration at 5 sites
                       indicate the same airmass pathway from
                                  the tropical Atlantic




             NOAAARL         Ad hoc Data Processing Value Chain
                ATAD
                              ATAD Traject
                              Gebhart (2002)
            Weather Serv.
            Upper Air Data
                                                CATT Tool     Aggregation
                                               Husar (2003)   Poirot (2003)
              PMF Tool
            Pareto (2001)
                              PMF “Sources”
                              Coutant (2002)
             NPS-CIRA
            IMPROVEData
    The Influence of Emissions,
    Dilution and Transformations
• The PM
  concentration, C, at
  any given location
  and time is
  determined by the
  combined
  interaction of
  emissions, E,
  atmospheric
  dilution, D, and
  chemical
  transformation and
  removal, T,
  processes:
       Seasonal Pattern of PM2.5
• The seasonal cycle results
  from changes in PM
  background levels,
  emissions, atmospheric
  dilution, and chemical
  reaction, formation, and
  removal processes.
• Examining the seasonal
  cycles of PM2.5 mass and
  its elemental constituents
  can provide insights into
  these causal factors.
• The season with the
  highest concentrations is a
  good candidate for PM2.5      Key reference: CAPITA

  control actions.
         Seasonal PM2.5 During 1988




• At Washington DC and Philadelphia,
  (Mid-Atlantic) the PM2.5                • At urban Southwestern sites,
  concentrations are 60% higher in          PM2.5 concentrations in the
  summer than in winter.
                                            winter are 50% higher than in
• In the rural Appalachians, the summer
  PM2.5 concentrations are a factor of      the summer.
  three higher than during the winter.    • At rural Southwestern sites,
                                            PM2.5 concentrations are 50%
                                                       Key reference: CAPITA
Regional Haze Goal: Attain natural
       conditions by 2064
         Pattern of Fires over N.
                 America peaks in warmsatellite-observed
                       The number of ATSR
                          fires           season
                             Fire onset and smoke amount is
                                 unpredictable




Fire Pixel Count:
  Western US




North America
Asian Dust Cloud over N.
       America       Asian Dust                 100 mg/m3




                             Hourly PM10
                     On April 27, the dust cloud arrived in
                     North America.

                     Regional average PM10
                     concentrations increased to 65 mg/m3

                     In Washington State, PM10
                     concentrations exceeded 100 mg/m3
      Origin of Fine Dust Events over
 Gobi dust in spring
                     the US
 Sahara in summer




Fine dust events over the
   US are mainly from
   intercontinental
   transport
      Daily Average Concentration
              over the US
Sulfate is seasonal with noise
Noise is by synoptic weather



        VIEWS Aerosol
      Chemistry Database




Dust is seasonal with
  noise
Random short spikes
  added
                   Sahara and Local Dust
           Apportionment: Annual and July
    The Sahara and Local dust was apportioned based on their respective source profiles.


                                July                                        Annual




•   In July the Sahara dust contributions are 4-8   • The maximum annual Sahara dust
    mg.m3                                             contribution is about 1 mg.m3
•   Throughout the Southeast, the Sahara dust       • In Florida, the local and Sahara dust
    exceeds the local source contributions by w       contributions are about equal but at Big
    wide margin (factor of 2-4)
                                                      Bend, the Sahara contribution is < 25%.
       Supporting Evidence: Transport
                  Analysis




The air masses arrive to Big Bend, TX    Satellite data (e.g. SeaWiFS) show Sahara
form the east (July) and from the west         Dust reaching Gulf of Mexico and
                (April)                              entering the continent.
Seasonal Fine Aerosol Composition,
  Upper Buffalo            Smoky Mtn

                E. US




  Big Bend, TX              Everglades, FL
Sahara PM10 Events over                               July 5, 1992

              Eastern US
Much previous work by Prospero, Cahill, Malm,
Scanning the AIRS PM10 and IMPROVE chemical
   databases several regional-scale PM10 episodes
   over the Gulf Coast (> 80 ug/m3) that can be
   attributed to Sahara.

                                                    June 30, 1993




                                                      June 21 1997


   The highest July, Eastern US, 90th
     percentile PM10 occurs over the Gulf
     Coast ( > 80 ug/m3)
   Sahara dust is the dominant contributor
         May 9, 1998 A Really Bad Aerosol Day for N. America

Asian Smoke

                                           Canada
                                             Smoke




What kind of                                 C. American
                                                Smoke
 neighborhood is
 this anyway?
           Seasonal PM2.5 Dependence on
        Elevation in Appalachian Mountains
  Monitor Locations and topography




• During August, the PM2.5 concentrations are independent of
  elevation to at least 1200 m. Above 1200 m, PM2.5 concentrations
  decrease.
• During January, PM2.5 concentrations decrease between sites at 300
  and 800 m by about 50% . PM2.5 concentrations are approximately
                                                  Key reference:
  constant from 800 m to 1200 m and decrease another ~50% from
          Local, Sahara and Gobi Dust           0.8

                                                                      Sahara     SW US
                                                0.7

                over N. America                 0.6

                                                0.5


                                                0.4

                                                0.3

                                                0.2

                                                0.1

                                                 0
                                                      Al/Si   Fe/Si      Ca/Si    K/Si   Ti/Si




• The dust over N. America originates from local sources as well as from the
  Sahara and Gobi Deserts

• Each dust source region has distinct chemical signature in the crustal elements.
    Fine Dust
    (<2.5mm)
    Local and
     Sahara
The two dust peeks at Big Bend have
different Al/Si ratios
During the year, Al/Si = 0.4
In July, Al/Si reaches 0.55, closer to the
Al/Si of the Sahara dust (0.65-0.7)
The spring peak is identified as as ‘Local
 •      In Florida, July peak is Fine
Dust’, while the virtually all thedominated by
        Particle Dust appears to originate
        dust.
Sahara from Sahara throughout the year

•      At other sites over the Southeast,
       Sahara dominates in July

•      T he Spring and Fall dust is evidently
       of local origin
  Supporting Evidence: Aerosol Pattern
        and Transport Analysis




There are large seasonal differences in the directions that air masses   •   In July (1998) elevated levels of absorbing aerosol (Sahara Dust) reaches the
arriving in Big Bend, TX have taken.                                         Gulf of Mexico and evidently, enters the continent .
During winter and into spring, they come from the west and the           •   High TOMS dust levels are seen along the US-Mexican borders, reaching
northwest,while during the summer, they come mainly from the east.           New Mexico. Higher levels also cover the Caribbean Islands and S. Florida.
                                                                         •   Another patch of absorbing aerosol (local dust?) is seen over the Colorado
                                                                             Plateau, well separated from the Sahara dust.
  Illustration of RAW: Quebec Smoke, July 6,
                     2002
Right. SeaWiFS satellite and METAR surface
    haze shown near-real time in the Voyager
    distributed data browser

Below. SeaWiFS, METAR and TOMS
    Absorbing Aerosol Index superimposed

Satellite data are fetched from NASA GSFC;
    surface data from NWS/CAPITA servers
Incremental Transport
     Probalility
   Analysis Value Chain: CATT’s
AE R O S O L  Habitat
      Collection          Integration              CATT-In        CATT CAT
      IMP. EPA             VIEWS                   CAPITA          CAPITA
Aerosol            Aerosol            Integrated             AerData     Aggreg.    Next
Sensors             Data               AerData                Cube Why? How?
                                                                         Aerosol   Process




 Weather            Gridded             Traject.                  When?Aggreg.
                                                             TrajData   Where?      Next
  Data              Meteor.              Data                 Cube     Traject.    Process

           Assimilate         Trajectory           CATT-In        CATT TAT
             NWS                ARL                CAPITA          CAPITA

TRAN S PO RT
   Transport Probability Metrics
• The transport metric is calculated from two
  residence time grids, one for all trajectories and
  another for trajectories on selected (filtered days).
  Both residence time grids are normalized by the
  sum of all resdence times in all grid cells:
             pijf=rij/SS rij       pija=rij/SS rij
• pijf, is the filtered and pija is the unfiltered
  residence time probabilitiy that an airmasses
  passes through a specific grid. There is a choice of
  transport probaility metrics:

• The Incremental Residence Time Probability
  (IRTP) proposed by Poirot et al., 2001 is obtained
  by subtracting the chemically filtered grid from
• Currently, there is a choice of two different transport
    Transport Metric Selection
  probability metrics:

• Incremental Residence Time Probability (IRTP)
  proposed by Poirot et al., 2001 is the difference
  between the chemically filtered and unfiltered
  residence time probalbilities. Positive values of
  IRTP in a grid indicates more than average liekihood
  of transport; (red); negative IRTP values (blue)
  represent less than average likeihood of transport.

• Potential Source Contribution Function (PSCF)
  proposed by Hopke et al., 19?? is computed as the
  ratio of the filtered and unfiltered residence time
                                   SUMMARY
•   The atmospheric dust system occupies at least 8 key dimensions
                    g (x, y, x, t, size, comp, shape, mixture)
•   The current observational revolution (satellites, surface networks) allows monitoring
    many aspects of the global daily aerosol pattern and transport.

•   Each sensor/system measures different aspects of aerosols, usually resolving some and
    integrating over other dimensions.

•   Data from multiple sensors/systems (satellites AND surface) along with models are
    required to characterize the 8D system and to derive actionable knowledge.

•   Current data and analysis tools allow the estimation of transcontinental transport of dust
    to N. America.

•   The yearly average fine (<2.5 um) Sahara dust concentration over the SE US is 0.2 – 1
    ug/m3, with July peak concentration of 2-6 ug/m3.

•   During specific transcontinental dust transport episodes from Africa and Asia, the
    globally transported surface dust concentrations approach 50-100 ug.m3 over 1000 km -
    scale regions of North America.
 SUMMARY: New Opportunities
• We are in the midst of a sensory revolution regarding the
  detection of global aerosol sources, transport and some of
  the effects. Satellite and surface network provide daily
  pattern of aerosol.

• Still, the available aerosol data provides only a sparse
  characterization of the aerosol system.

• The Internet facilitates communication and the sharing,
  (reuse) of data and tools. There is a growing
  collaborative-sharing spirit in the scientific community;
  The winds of change are here – but we need to harness
  them for faster learning
      Combined Aerosol
   Trajectory Tool (CATT)
     Example: Airmass origin for high
                     (2.5*average) nitrate




Boundary Waters                           Doly Sods




Lye Brook                                 Smoky Mtn.

            Triangulation indicates nitrate source in the corn belt
CATT: A Community                                                                           Not There!
                                                                                   Further Analysis
                                                                                      When?
      Tool!                                                                           Where?
                                                                                          GIS

 Part of an Analysis Value                                                           Grid Processing
                                                                                        Emission
                                                                                       Why?
                                                                                   There!
           Chain                                                                       Comparison
                                                                                       How?

AEROSOL
      Collection          Integration              CATT-In
      IMP. EPA             VIEWS                   CAPITA
Aerosol            Aerosol            Integrated             AerData    Aggreg.         Next
Sensors             Data               AerData                Cube      Aerosol        Process

                                                                   CATT
 Weather            Gridded             Traject.             TrajData   Aggreg.         Next
  Data              Meteor.              Data                 Cube      Traject.       Process

           Assimilate         Trajectory           CATT-In
             NWS                ARL                CAPITA

TRANSPORT
                         Haze by RPO
      Judged qualitatively based on
          WRAP                                     MANE-VU
    current surface and satellite data
   Local Smoke                                Canada Smoke
   Local Dust
   Asian Dust




         CENRAP                  MRPO               VISTAS
   Local Smoke            Local Smoke         Local Smoke
   Mexico/Canada Smoke    Canada Smoke        Sahara Dust
   Local Dust             Local Dust
   Sahara Dust


• Natural forest fires and windblown dust are judged to be the key
  contributors to regional haze
• The dominant natural sources include locally produced and long-
    Scientific Challenge: Description
Particulate matter is complex because of its multi-dimensionality

                                     of PM
It takes at leas 8 independent dimensions to describe the PM concentration pattern


                   Dimension              Abbr   Data Sources
                   Spatial dimensions     X, Y   Satellites, dense networks
                                          .
                   Height                 Z      Lidar, soundings
                   Time                   T      Continuous monitoring
                   Particle size          D      Size-segregated sampling
                   Particle Composition   C      Speciated analysis
                   Particle Shape/Form    F      Microscopy
                   Ext/Internal Mixture   M      Microscopy




  • Gaseous concentration:       g (X, Y, Z, T)
  • Aerosol concentration:              a (X, Y, Z, T,
    D, C, F, M)
  • The ‘aerosol dimensions’ size D, composition C,
    shape F, and mixing M determine the impact on
    health, and welfare.
             Technical Challenge:
•   PM characterization requires many different
                 Characterization
    instruments and analysis tools.
• Each sensor/network covers only a limited fraction
  of the 8-D PM data space.
• Most of the 8D PM pattern is extrapolated from
  sparse measured data.
• Some devices (e.g. single particle electron
  microscopy) measure only a small subset of the
  PM; the challenge is extrapolation to larger space-
  time domains.
• Others, like satellites, integrate over height, size,
  composition, shape, and mixture dimensions; these
  data need de-convolution of the integral measures.
              Data Analysis and Decision
                       Retrospective Anal.                 Now Analysis                         Predictive Analysis
                                                           Days                                         Days-years

                       Support
                      Months-years

Data Sources &         All the Real-Time data +            EPA PM2.5Mass                       NAAPS MODEL Forecast
                       NPS IMPROVE Aer. Chem.              NWS ASOS Visibility, WEBCAMs        NOAA/EPA CMAQ?
Types                  EPA Speciation                      NASA MODIS, GOES, TOMS, MPL
                       EPA PM10/PM2.5                      NOAA Fire, Weather & Wind
                       EPA CMAQ Full Chem. Model           NAAPS MODEL Simulation
Data Analysis Tools    Full chemical model simulation      Spatio-temporal overlays            Emission and met. forecasts
                       Diagnostic & inverse modeling       Multi-sensory data integration      Full chemical model
& Methods              Chemical source apportionment       Back & forward trajectories, CATT   Data assimilation
                       Multiple event statistics           Pattern analysis                    Parcel tagging, tracking


Communication          Tech Reports for reg. support       Analyst and managers consoles       Open, public forecasts
                       Peer reviewed scientific papers     Open, inclusive communication       Model-data comparison
Collab. & Coord.       Science-AQ mgmt. interaction        Data assimilation methods           Modeler-data analyst comm.
Methods                Reconciliation of perspectives      Community data & idea sharing

Analysis Products      Quantitative natural aer. concr.    Current Aerosol Pattern             Future natural emissions
                       Natural source attribution          Evolving Event Summary              Simulated conc. pattern
                       Comparison to manmade aer.          Causality (dust, smoke, sulfate)    Future location of high conc.

Decision Support       Jurisdiction: nat./manmade          Jurisdiction: nat./manmade          Statutory & policy changes
                       State Implementation Plans, (SIP)   Triggers for management action      Management action triggers
                       PM/Haze Crit. Documents, Regs       Public information & decisions      Progress tracking
        July 2020 Quebec Smoke Event
–




    Superposition of ASOS         •   PM2.5 time series for New England sites.
                                      Note the high values at White Face Mtn.
      visibility data (NWS) and   •   Micropulse Lidar data for July 6 and July 7,
      SeaWiFS reflectance data        2002 - intense smoke layer over D.C. at 2km
                                      altitude.
      for July 7, 2002
GLAS Satellite Lidar (Geoscience Laser
             Altimeter System)
  California Fires, Oct for continuous global
First satellite lidar 7, 2003
            observations of Earth
   Quebec
   Smoke
  over the
 Northeast
Smoke
  (Organics) and
 Sulfate
 concentration
 data from
 VIEWS
 integrated
 database
DVoy overlay of
 sulfate and
 organics
 during the

				
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