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2005-10-31 Satellite Aerosol Climatology
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2005-10-31 Satellite Aerosol Climatology

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  • Transcript

    • 1. Satellite Aerosol Climatology
    • 2. Satellite – Surface Visibility Analysis, 1976
      • First satellite aerosol paper, F. Parmenter, 1972
      • The relationship to surface aerosol was qualitatively established
      Regional Haze Low Visibility Hazy ‘Blobs’ Lyons W.A., Husar R.B. Mon. Weather Rev. 1976 SMS GOES June 30 1975
    • 3. Smoke Quantification is Elusive! Satellite Integral: Height, Size, Composition
        • Satellite Smoke Sensing Issues
        • Clouds, snow, night – no data
        • Column AOT can be optically thick
        • Smoke reflectance (blue, yellow, white) is hard to interpret
    • 4. Smoke Plumes over the Southeast
      • Satellite detection yields the origin and location is the shape of smoke plumes
      • The influence of the smoke is to increase the reflectance ant short wavelength (0.4 mm)
      • At longer wavelength, the aerosol reflectance is insignificant.
      R 0.68  m G 0.55  m B 0.41  m 0.41  m 0.87  m
    • 5.  
    • 6. 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
    • 7. ASOS Surface Bext 1200 ASOS Stations
    • 8. Monte Carlo Dispersion Simulation Surface wind 1200 Station ASOS MM5? z T 0 1 km MIDDAY NIGHT MORNING Mixing depth Subsidence inversion
    • 9. 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’
    • 10.  
    • 11. SeaWiFS AOT – Summer 60 Percentile 1 km Resolution
    • 12.  
    • 13.  
    • 14. Seasonal Surface Reflectance, Eastern US
      • April 29, 2000, Day 120
      July 18, 2000, Day 200 October 16, 2000, Day 290
    • 15. Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 99 Percentile 90 Percentile 60 Percentile
    • 16. Satellite AOT – Time Fraction (0-100%) SeaWiFS Satellite, Summer 2000 - 2003 Dec, Jan Feb Sep, Oct, Nov Jun, Jul, Aug Mar, Apr, May
    • 17. Information Techology Vision Scenario: Smoke Impact REASoN Project: Application of NASA ESE Data and Tools to Particulate Air Quality Management ( PPT/PDF )
      • Scenario:
      • Smoke form Mexico causes record PM over the Eastern US.
      • Goal:
      • Detect smoke emission and predict PM and ozone concentration
      • Support air quality management and transportation safety
      • Impacts:
      • PM and ozone air quality episodes, AQ standard exceedance
      • Transportation safety risks due to reduced visibility
      • Timeline:
      • Routine satellite monitoring of fire and smoke
      • The smoke event triggers intensified sensing and analysis
      • The event is documented for science and management use
      • Science/Air Quality Information Needs:
      • Quantitative real-time fire & smoke emission monitoring
      • PM, ozone forecast (3-5 days) based on smoke emissions data
      • Information Technology Needs:
      • Real-time access to routine and ad-hoc data and models
      • Analysis tools: browsing, fusion, data/model integration
      • Delivery of science-based event summary/forecast to air quality and aviation safety managers and to the public
      Record Smoke Impact on PM Concentrations [email_address] , [email_address] Smoke Event
    • 18. Satellite Data Us in PM Management: A Retrospective Assessment Rudolf B. Husar CAPITA, Washington University Presented at A&WMA’s 97 th Annual Conference and Exhibition June 22-27, Indianapolis, IN MexicanSmoke
    • 19. 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
    • 20. Current Air Quality Information Ecosystem: ‘Organic’ Future Federated Air Quality Information System DataMart VIEWS NEISGEI AIRNow AQMod DAACs ASOS NEI Emission IDEA GASP Missions WeaMod Forecast GloMod FireInv Data Federation Distributed, Uniform AQ Forecasting AQ Compliance Status and Trends Network Assess. Data Processing Filtering, Aggregation, Fusion Info Products Reports, Websites Mediators
    • 21. 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 Networking Reuse Non-intrusive Linking & Mediation Inform Public AQ Compliance Forecast AQ Status & Trends Satellite Devel. Network Asses. Manage Hazards ……… Info Needs Reports Data Users Emission Surface Satellite Model Single Datasets Providers Data Providers
    • 22. DataFed Apprach
      •  
      • DataFed assumes spontaneous, autonomous emergence of AQ data ( a la nodes on Internet)
      • Non-intrusively wraps datasets for access by web services
      • WS-based mediators provide homogeneous data views e.g. geo-spatial, time...
      • Programming by End-user through Web Service composition  
    • 23.
      • SeaWiFS Satellite
      SeaWiFS Satellite Aerosol Chemical Air Trajectory Map Boarder VIEW by Web Service Composition
    • 24. 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
    • 25. Summary
      • Major advances in fire detection (fire pixels, burn scars)
      • Satellite and surface smoke detection is also advanced
      • Still smoke quantification is elusive
      • Need to integrate the available sensory information
      • Propose an IT – mediated collaborative approach