http://datafedwiki.wustl.edu/index.php/2006-10-18_SeaWiFS_Satellite_Work

4. Aerosols Increase of Decrease the Surface Reflectance, f(P/R 0 ) Aerosols will increase the apparent surface reflectance, R, if P/R 0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ. When P/R 0 > 1, aerosols will decrease the surface reflectance. Accordingly, the brightness of clouds is reduced by overlying aerosols. At P~ R 0 the reflectance is unchanged by haze aerosols (e.g. soil and vegetation at 0.8 um). . At large τ (radiation equilibrium), both dark and bright surfaces asymptotically approach the ‘aerosol reflectance’, P The critical parameter influencing the apparent reflectance, R, is the ratio of aerosol phase function (angular reflectance), P, to bi-directional surface reflectance, R 0 , (P/ R 0)

5. Haze Effect on Spectral Reflectance over Land The spectral reflectance of vegetation in the visible λ is low at 0.01<R 0 <0.1. Haze significantly enhances the reflectance in the blue but the haze excess in the near IR is small. This is consistent with radiative transfer theory of haze impact.

7. 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

8. April 12, 2003 April 10, 2003 Kansas Smoke

9. Kansas Smoke Emission Estimation April 11: 87 T/day April 10: 1240 T/d Assuming Mass Extinction Efficiency: 5 m 2 /g April 11, 2003 Emission Estimate Fire Pixels Surface Observations Monte Carlo Diagnostic Local Model

13. Yesterday’s Smoke and Today’s Smoke Yesterday’s smoke is not in a plume “worm” shape and it has moved a distance Today’s smoke is distinct plumes

15. Satellite-Surface-Model Data Integration Smoke Emission Estimation 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 Location Assimilated Fire Location Emission Model Land Vegetation Fire Model

16. Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 98 Percentile 90 Percentile 60 Percentile Smoke Sources 98 Percentile

18. Abstract The main scientific challenge in the study of particulate matter (natural or man made) is to understand the immense structural and dynamic complexity of the 6-dimensional aerosol system (X, Y, Z, T, Diameter, Composition). Each sensor/network covers only a limited fraction of the 8-D data space; some measure only a small subset of the PM pollution data and need extrapolating. Others provide broad integral measures of the aerosol system Satellites, for example, integrate over atmospheric height, particle size, composition, shape, and mixture dimensions. The interpretation of these integral data requires considerable de-convolution of the integral measures. Given its many dimensional properties, the aerosol system is largely self-describing. The analyst's challenge is to derive the pattern of dust, smoke, haze by filtering, aggregating and fusing the multidimensional data. The paper shows recent results of aerosol characterization using seven years of SeaWiFS-derived data over the US, along with companion surface observations along with surface PM chemical and physical data.