20051031 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis
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20051031 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis

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20051031 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis 20051031 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis Presentation Transcript

  • Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis R.B. Husar Washington University in St. Louis Presented at NARSTO Workshop on Innovative Methods for Emission-Inventory Development and Evaluation Austin, TX ; October 14-17, 2003
  • Pattern of Fires over N. America
    • The number of ATSR satellite-observed fires peaks in warm season
    • Fire onset and smoke amount is unpredictable
    Fire Pixel Count: Western US North America
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  • Scientific Challenge: Description of smoke
    • 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.
    Particulate matter, incl. smoke is complex because of its multi-dimensionality 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
  • Technical Challenge: Characterization
    • PM characterization requires many different instruments and analysis tools.
    • Each sensor/network covers only a fraction of the 8-D PM data space .
    • Most of the 8D PM pattern is extrapolated from sparse measured data.
    • Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.
    Satellite-Integral
  • Smoke types: blue, yellow, white
      • Smoke from major fires comes in different colors, e.g. blue, yellow.
      • The chemical, physical and optical characteristics of smokes are not known
      • Can the reflectance color be used to classify smokes?
      • Can column AOT be retrieved for optically thick smoke? Multiple scattering, absorption?
    California Smoke 1999 Quebec Smoke 2002
  • 2002 Quebec Smoke GOES East & ASOS Bext & MPL Lidar July 6, 2002 8:15, 16:15 EST
  • Smoke Plumes over the Southeast
    • Satellite detection yields the origin and location is the shape of smoke plumes
    R 0.68  m G 0.55  m B 0.41  m 0.41  m 0.87  m
    • The influence of the smoke is to increase the reflectance ant short wavelength (0.4  m )
    • At longer wavelength, the aerosol reflectance is insignificant.
  • Real-Time Smoke Emission Estimation: Local Smoke Model with Data Assimilation e..g. MM5 winds, plume model Local Smoke Simulation Model AOT Aer. Retrieval Satellite Smoke Visibility, AIRNOW Surface Smoke Assimilated Smoke Pattern Continuous Smoke Emissions Assimilated Smoke Emission for Available Data Fire Pixel, Field Obs Fire Loc, Energy Assimilated Fire Location, Energy NOAA, NASA, NFS NOAA, NASA, NFS NOAA, EPA, States Emission Model Land Vegetation Fire Model Regional AQ Model
  • Kansas Agricultural Smoke, April 12, 2003 Fire Pixels PM25 Mass, FRM 65 ug/m3 max Organics 35 ug/m3 max Ag Fires SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue
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  • ASOS Visibility Monitoring System (1200 Sites)
    • The Automated Surface Observing System, ASOS; weather every minute.
    • The forward scattering (30-50 0 ) visibility sensor has a range 17 ft to 30 miles.
    • The synoptic visibility data are truncated (<1/4, 1/4,..10+ miles)
    • For smoke and haze events (vis. < 10 mile) truncation not a problem
  • Diurnal Cycle – Surface Bext, April 12, 2003 00 02 04 06 08 10 12 14 16 18 20 22 Night Day Night High Night Bext Low Day Bext Smoke
  • 0504142345 HMS + GOES
  • 0504142345 HMS + GOES
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  • Current Air Quality Information ‘Ecosystem’ (Draft for Feedback)
    • AQ information includes emissions , ambient & satellite data and model outputs
    • The distributed data are produced and provided by agencies , mostly through portals
    • Providers have different access protocols , formats, and information usage conditions
    • This lack of interoperability causes the under-utilization of the rich data resources
  • Future Integrated AQ information System (Draft for Feedback)
    • Data are maintained by custodians and exposed through ‘portals’
    • Mediators uniformly ‘wrap’ data and provide processing services
    • Analysts program the services to create application-specific products
    • Responsibility is shared among data providers and mediator/ integrators
    • ESIPFed can provide the infrastructure and tools for the AQ info system
    DataMart VIEWS NEISGEI AIRNow AQMod DAACs ASOS NEI Emission IDEA GASP Missions WeaMod Forecast GloMod FireInv Data Federation Distributed, Virtual, Uniform AQ Forecasting AQ Compliance Status and Trends Network Assess. Data Processing Filtering, Aggregation, Fusion Info Products Reports, Websites Mediators
  • Federated Air Quality Data System - Draft
    • Text 1
    Text 2 ESIP AQ Cluster 050510 Draft [email_address] Run and click PPT Slideshow to see chart animations Wrappers Where? What? When? Federate Data Structuring Slice & Dice Explore Data Viewers Programs Integrate Understand Inform Public AQ Compliance Forecast AQ Status & Trends Satellite Devel. Network Asses. Manage Hazards ……… Info Needs Reports Emission Surface Satellite Model Single Datasets Providers Networking Reuse Non-intrusive Linking & Mediation Data Users Data Providers
  • DataFed Description
    • DataFed Vision
    • Better air quality management and science through by effective use of relevant data
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    • DataFed Goals
    • Facilitate the access and flow of atmospheric data from provider to users
    • Support the development of user-driven data processing value chains
    • P articipate in specific application projects
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    • Approach: Mediation Between Users and Data Providers
    • DataFed assumes spontaneous, autonomous emergence of AQ data ( a la Internet)
    • Non-intrusively wraps datasets for access by web services
    • WS-based mediators provide homogeneous data views e.g. geo-spatial, time...
    • End-user programming of data access and processing through WS composition (limited)
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    • Applications
    • Building browsers and analysis tools for distributed monitoring data   
    • Serve as data gateway for user programs; web pages, GIS, science tools
    • DataFed is currently focused on the mediation of air quality data
  • Anatomy of a Wrapper Service: TOMS Satellite Image Data
    • Given the URL template and the image description, the wrapper service can access the image for any day, any spatial subset using a HTTP URL or SOAP protocol:
    • Wrapper classes are available for geo-spatial (incl. satellite) images, SQL servers, text files,etc. The mediator classes are implemented as web services for uniform data access, transformation and portrayal.
    src_img_width src_img_height src_margin_right src_margin_left src_margin_top src_margin_bottom src_lon_min src_lat_max src_lat_min src_lon_max Image Description for Data Access: src_image_width=502 src_image_height=329 src_margin_bottom=105 src_margin_left=69 src_margin_right=69 src_margin_top=46 src_lat_min=-70 src_lat_max=70 src_lon_min=-180 src_lon_max=180 The daily TOMS images reside on the FTP archive, e.g. ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y2000/ea000820.gif URL template: ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/ea[yy][mm][dd].gif Transparent colors for overlays RGB(89,140,255) RGB(41,117,41) RGB(23,23,23) RGB(0,0,0)
    • SeaWiFS Satellite
    SeaWiFS Satellite Aerosol Chemical Air Trajectory Map Boarder VIEW by Web Service Composition
  • Datasets Used in FASTNET
    • Data are accessed from autonomous, distributed providers
    • DataFed ‘wrappers’ provide uniform geo-time referencing
    • Tools allow space/time overlay, comparisons and fusion
    Near Real Time Data Integration Delayed Data Integration Surface Air Quality AIRNOW O3, PM25 ASOS_STI Visibility, 300 sites METAR Visibility, 1200 sites VIEWS_OL 40+ Aerosol Parameters Satellite MODIS_AOT AOT, Idea Project GASP Reflectance, AOT TOMS Absorption Indx, Refl. SEAW_US Reflectance, AOT Model Output NAAPS Dust, Smoke, Sulfate, AOT WRF Sulfate Fire Data HMS_Fire Fire Pixels MODIS_Fire Fire Pixels Surface Meteorology RADAR NEXTRAD SURF_MET Temp, Dewp, Humidity… SURF_WIND Wind vectors ATAD Trajectory, VIEWS locs.
  • A Sample of Datasets Accessible through ESIP Mediation Near Real Time (~ day)
    • It has been demonstrated (project FASTNET) that these and other datasets can be accessed, repackaged and delivered by AIRNow through ‘Consoles’
    MODIS Reflectance MODIS AOT TOMS Index GOES AOT GOES 1km Reflec NEXTRAD Radar MODIS Fire Pix NRL MODEL NWS Surf Wind, Bext
  • FASTNET: Inter-RPO pilot project, through NESCAUM, 2004 Web-based data, tools for community use Built on DataFed infra-structure, NSF, NASA Project fate depends on sponsor, user evaluation
  • Some of the Tools Used in FASTNET
      • Data Catalog
      • Data Browser
      • PlumeSim, Animator
      • Combined Aerosol Trajectory Tool (CATT)
    Consoles: Data from diverse sources are displayed to create a rich context for exploration and analysis CATT: Combined Aerosol Trajectory Tool for the browsing backtrajectories for specified chemical conditions Viewer: General purpose spatio-temporal data browser and view editor applicable for all DataFed datasets
  • Midwest HazeCam Image Console Image Archive and Browser
    • Hourly Midwest HazeCam Images are archived by DataFed data access system
    • Archived images for all cameras can be browsed through this console
    • HazeCam URL for a day: http://www.datafed.net/consoles/MWH_WebCams.asp?image_width=400&image_height=300&datetime=2005-01-31T13:00:00
    • URL for a site and day: http://webapps.datafed.net/datasets/webcam/cincinnati/20050131-13mwhcincinnati.jpg
    • URLs can be embedded as links into emails, bookmarks, web pages, PPT and PDF files.
    Midwest HazeCam Image Browser Select date and time Set image size and time MW HazeCam Console Other FASTNET Consoles
  • Aerosol Event Catalog: Web pages
    • Catalog of generic ‘web objects’ – pages, images, animations that relate to aerosol events
    • Each ‘web object’ is cataloged by location, time and aerosol type.
  • DIURNAL CYCLE OF SURFACE HEATING/COOLING: z T 0 1 km MIDDAY NIGHT MORNING Mixing depth Subsidence inversion NIGHT MORNING AFTERNOON
  • 2000-2004 SeaWiFS Satellite AOT Daily and Climatology, 1 km resolution Ready to be used by the community! Bad Data Idaho & Cal Smoke 5 Year Median AOT, JJA EUS Haze Atlanta Appalachian AOT ‘hole’
  • Seasonal Surface Reflectance, Eastern US
    • April 29, 2000, Day 120
    July 18, 2000, Day 200 October 16, 2000, Day 290
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