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2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
2003-08-27 Data Fusion for the Description and Explanation of Atmospheric Aerosols
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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  • 1. Data Fusion for the Description and Explanation of Atmospheric Aerosols <ul><li>R. B. Husar and S.R. Falke   Center for Air Pollution Impact and Trend Analysis (CAPITA) </li></ul><ul><ul><li>Washington University, St. Louis, MO </li></ul></ul><ul><li>Prepared for Presentation at </li></ul><ul><ul><li>8 th Int. Assoc. of Meteorology and Atm. Sciences, IAMAS Assembly </li></ul></ul><ul><ul><li>Innsbruck, Austria, 10-18 July, 2001 </li></ul></ul>
  • 2. Objectives of Atmospheric Aerosol Data Fusion : <ul><li>Description: What, Where, When </li></ul><ul><li>Aerosol is a 6+ dimensional system (x, y, z, t, size, composition ). </li></ul><ul><li>Each sensor samples a small subset of the aerosol data space; or some integral measures of the 6D aerosol distribution </li></ul><ul><li>Combination of sensors yields a somewhat more complete aerosol pattern in space, time, size, composition </li></ul><ul><li>Explanation: Why, How </li></ul><ul><li>The explanation establishes causality of pattern through processes . </li></ul><ul><li>Processes include emission, transport, transformations and removal </li></ul><ul><li>Combined data on processes yields the science, i.e. the explanation </li></ul>
  • 3. Sensor, Pixel and Feature-Level Fusion <ul><li>Sensor-level fusion is taking place in the instrument or early data processing </li></ul><ul><li>Pixel-level fusion combines data from multiple ‘platforms’; requires georeferencing, time referencing </li></ul><ul><li>Feature-level fusion requires extraction of features, i.e. pattern </li></ul><ul><li>The ‘object’ is described by its features </li></ul>
  • 4. Data Fusion for Enhanced Description (Pattern) <ul><li>Spatial Pattern and Height of Asian Dust </li></ul><ul><li>Spatial Pattern and Height of Sahara Dust </li></ul><ul><li>Elevation of Central American Smoke </li></ul>
  • 5. Gobi Dust, Apr 15, 19 1998 <ul><li>SeaWiFS records columnar AOT; TOMS is sensitive to upper layer dust. </li></ul><ul><li>Aerosol features derived from the combination of SeaWiFS, TOMS, Winds Speed and Visibility data: </li></ul><ul><li>On April 15 the dust plumes over Gobi are seen in SeaWiFS but not on TOMS. </li></ul><ul><li>On April 19 both satellite sensors and surface visibility indicates dust. </li></ul><ul><li>Hence, the April 16 plume is surface-based while the April 19 plume reaches from ground to high elevation. </li></ul>
  • 6. Topography: Sahara Dust Near the Surface <ul><li>SeaWiFS shows a dense dust layer emanating from W. Africa </li></ul>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 <ul><li>Visibility is recorded at 7000+ surface stations hourly </li></ul>
  • 8. In West-Central Africa, winter haze is surface- based; summer haze is elevated <ul><li>In Jan-Feb the horizontal (Bext) extinction and vertical optical thickness (AOT) are correlated. </li></ul><ul><li>This implies that the haze is surface-based and has a scale-height of of about 1 km. </li></ul><ul><li>In Jun-Jul, the Bext is below detection limit, while the AOT is the same as in Jan-Feb. </li></ul><ul><li>Evidently, the summer dust layer is elevated while the surface layer is dust-free. </li></ul>Based on AERONET Sun Photometer Network, NOAA SOD Visibility data
  • 9. Central American Smoke, May 1998 <ul><li>On May 14, 15 SeaWiFS shows dense smoke over S. Mexico; low TOMS -&gt; lower level smoke </li></ul><ul><li>On May 14, high SeaWiFS, TOMS and Bext over Texas -&gt; smoke at low and hi elevation </li></ul><ul><li>On May 15, SeaWiFS, TOMS and Bext coincide over EUS -&gt; well mixed surface smoke </li></ul>
  • 10. Topography: Smoke Confined to Low Elevations <ul><li>On May 14, C. American smoke was confined to low elevations </li></ul><ul><li>The high plateau extrudes from smoke </li></ul><ul><li>In California, Oct 18 99, smoke in the Sierras fills the the Central Valley </li></ul><ul><li>Smoke exits to the Pacific through a gap </li></ul>
  • 11. Data Fusion for Explanation of Causality <ul><li>Emission Region of Elevated Sahara Dust, JJA </li></ul><ul><li>Spatial Pattern and Height of Sahara Dust </li></ul><ul><li>Elevation of Sahara Dust and Biomass Smoke </li></ul><ul><li>Elevation of Central American Smoke </li></ul>
  • 12. Dust Dunes based on Satellite Radar <ul><li>NASA NSCAT Program </li></ul><ul><li>Low surface roughness from 2 cm radar indicates sand dunes smooth surface (red) </li></ul><ul><li>Extensive dunes are evident over Sahara, S. Arabia and E. Central Asia </li></ul>
  • 13. Dust Source Regions <ul><li>NASA TOMS and NSCAT </li></ul>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) <ul><li>Arid regions (deserts) and rainforests are void of biomass fires </li></ul><ul><li>Fires are confined to narrow range of NDVI) </li></ul>
  • 15. <ul><li>FIRE and Norm. Diff. Veg. Index, NDVI </li></ul><ul><li>The ‘Northern’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires. </li></ul><ul><li>The ‘Northwestern’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires </li></ul><ul><li>The ‘Southeastern’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires. </li></ul><ul><li>The ‘Mexican’ zone is the most intense fire zone, sharply eparated from arid and the lush regions. </li></ul><ul><li>Fires are absent in arid low-vegetation areas (yellow) and over areas of heavy, moist vegetation (blue). </li></ul>Fire Zones of North America
  • 16. Fires and Elevation Western US (1996-2000) <ul><li>Fire locations over the Western US are mainly at higher elevations </li></ul>
  • 17. Resistances to Data Fusion: Mechanical and ‘Other’ <ul><li>Data are increasingly exposed through the Internet but how do I find it and combine it with other data? </li></ul><ul><li>Need a catalog the aerosol-related resources for finding the resources. </li></ul><ul><li>Need rudimentary standards to help data flow from producers through value-adding processors to to the consumers . </li></ul><ul><li>Time for a Global Aerosol Information Network, GAIN ? </li></ul>

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