2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
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2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols






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2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols Presentation Transcript

  • 1. Data Fusion for the Description and Explanation of Atmospheric Aerosols
    • R. B. Husar and S.R. Falke   Center for Air Pollution Impact and Trend Analysis (CAPITA)
      • Washington University, St. Louis, MO
    • Prepared for Presentation at
      • 8 th Int. Assoc. of Meteorology and Atm. Sciences, IAMAS Assembly
      • Innsbruck, Austria, 10-18 July, 2001
  • 2. Objectives of Atmospheric Aerosol Data Fusion :
    • Description: What, Where, When
    • Aerosol is a 6+ dimensional system (x, y, z, t, size, composition ).
    • Each sensor samples a small subset of the aerosol data space; or some integral measures of the 6D aerosol distribution
    • Combination of sensors yields a somewhat more complete aerosol pattern in space, time, size, composition
    • Explanation: Why, How
    • The explanation establishes causality of pattern through processes .
    • Processes include emission, transport, transformations and removal
    • Combined data on processes yields the science, i.e. the explanation
  • 3. Sensor, Pixel and Feature-Level Fusion
    • Sensor-level fusion is taking place in the instrument or early data processing
    • Pixel-level fusion combines data from multiple ‘platforms’; requires georeferencing, time referencing
    • Feature-level fusion requires extraction of features, i.e. pattern
    • The ‘object’ is described by its features
  • 4. Data Fusion for Enhanced Description (Pattern)
    • Spatial Pattern and Height of Asian Dust
    • Spatial Pattern and Height of Sahara Dust
    • Elevation of Central American Smoke
  • 5. Gobi Dust, Apr 15, 19 1998
    • SeaWiFS records columnar AOT; TOMS is sensitive to upper layer dust.
    • Aerosol features derived from the combination of SeaWiFS, TOMS, Winds Speed and Visibility data:
    • On April 15 the dust plumes over Gobi are seen in SeaWiFS but not on TOMS.
    • On April 19 both satellite sensors and surface visibility indicates dust.
    • Hence, the April 16 plume is surface-based while the April 19 plume reaches from ground to high elevation.
  • 6. Topography: Sahara Dust Near the Surface
    • SeaWiFS shows a dense dust layer emanating from W. Africa
    3D View 3D view shows that shallow islands are submerged in dust, while high islands extrude from the ~1 km deep dust layer
  • 7. Continental Surface Visual Range, Extinction Coefficient
    • Visibility is recorded at 7000+ surface stations hourly
  • 8. In West-Central Africa, winter haze is surface- based; summer haze is elevated
    • In Jan-Feb the horizontal (Bext) extinction and vertical optical thickness (AOT) are correlated.
    • This implies that the haze is surface-based and has a scale-height of of about 1 km.
    • In Jun-Jul, the Bext is below detection limit, while the AOT is the same as in Jan-Feb.
    • Evidently, the summer dust layer is elevated while the surface layer is dust-free.
    Based on AERONET Sun Photometer Network, NOAA SOD Visibility data
  • 9. Central American Smoke, May 1998
    • On May 14, 15 SeaWiFS shows dense smoke over S. Mexico; low TOMS -> lower level smoke
    • On May 14, high SeaWiFS, TOMS and Bext over Texas -> smoke at low and hi elevation
    • On May 15, SeaWiFS, TOMS and Bext coincide over EUS -> well mixed surface smoke
  • 10. Topography: Smoke Confined to Low Elevations
    • On May 14, C. American smoke was confined to low elevations
    • The high plateau extrudes from smoke
    • In California, Oct 18 99, smoke in the Sierras fills the the Central Valley
    • Smoke exits to the Pacific through a gap
  • 11. Data Fusion for Explanation of Causality
    • Emission Region of Elevated Sahara Dust, JJA
    • Spatial Pattern and Height of Sahara Dust
    • Elevation of Sahara Dust and Biomass Smoke
    • Elevation of Central American Smoke
  • 12. Dust Dunes based on Satellite Radar
    • NASA NSCAT Program
    • Low surface roughness from 2 cm radar indicates sand dunes smooth surface (red)
    • Extensive dunes are evident over Sahara, S. Arabia and E. Central Asia
  • 13. Dust Source Regions
    Sand dunes from NSCAT The combined TOMS and NSCAT satellite data indicates: In JJA, the largest source of high-elevation dust is in Mali-W. Africa.
  • 14. Biomass Fires (ESA-IONIA) and Vegetation (NDVI)
    • Arid regions (deserts) and rainforests are void of biomass fires
    • Fires are confined to narrow range of NDVI)
  • 15.
    • FIRE and Norm. Diff. Veg. Index, NDVI
    • The ‘Northern’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires.
    • The ‘Northwestern’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires
    • The ‘Southeastern’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires.
    • The ‘Mexican’ zone is the most intense fire zone, sharply eparated from arid and the lush regions.
    • Fires are absent in arid low-vegetation areas (yellow) and over areas of heavy, moist vegetation (blue).
    Fire Zones of North America
  • 16. Fires and Elevation Western US (1996-2000)
    • Fire locations over the Western US are mainly at higher elevations
  • 17. Resistances to Data Fusion: Mechanical and ‘Other’
    • Data are increasingly exposed through the Internet but how do I find it and combine it with other data?
    • Need a catalog the aerosol-related resources for finding the resources.
    • Need rudimentary standards to help data flow from producers through value-adding processors to to the consumers .
    • Time for a Global Aerosol Information Network, GAIN ?