1. Aerosol Characterization Using the SeaWiFS Sensor and Surface Data E. M. Robinson and R. B. Husar Washington University, St. Louis, MO 63130

2. Technical Challenge: Aerosol Characterization PM characterization needs many different instruments and analysis tools Each sensor/network covers only a fraction of the 6-Dim PM data space (X, Y, Z, T, Diameter, Composition) Most of the 6D pattern is extrapolated from sparse measured data Satellite-Integral Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.

3. Co-Retrieval of Surface and Aerosol Properties Apparent Surface Reflectance, R Aer. Transmittance Both R 0 and R a are attenuated by aerosol extinction T a which act as a filter Aerosol Reflectance Aerosol scattering acts as reflectance, R a adding ‘airlight’ to the surface reflectance Surface Reflectance The surface reflectance R 0 is an inherent characteristic of the surface R = (R 0 + (e -  – 1) P) e -  The surface reflectance R 0 objects viewed from space is modified by aerosol scattering and absorption. The apparent reflectance, R, is: R = (R 0 + R a ) T a Aerosol as Reflector: R a = (e -  – 1) P Aerosol as Filter: T a = e -  Apparent Reflectance R may be smaller or larger then R 0, depending on aerosol reflectance and filtering.

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.

6. Co-Retrieval: Seasonal Surface Reflectance, Eastern US April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290

7. Kansas Agricultural Smoke, April 12, 2003 Fire PixelsPM25 Mass, FRM 65 ug/m3 max Organics 35 ug/m3 max Ag Fires SeaWiFS, ReflSeaWiFS, AOT ColAOT 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

10. Find Total Concentration -Used Blue band (.4um) because smoke has the strongest signal here -By eyeball, at first identifying ‘worm’ shape in the AOT -Using cross sections or profiles to better see the edge of the plume -Stretched image so that the plume is actually cut out

11. Background Concentration -Identified individual halos using a small pixel margin around each plume edge -Background changes from area to area

12. Quantification of smoke using AOT For each area circled there is an average column concentration (mass/area) With the total and background concentrations the plume concentration is: –Plume Concentration = Total – Background Multiplying by the concentration by its area gives the mass of each plume The sum of the plumes is the amount accumulated up to 12pm when the satellite passes over Total Mass = 87 tons

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

14. Quantifying yesterday’s smoke Using the same procedure we calculated the amount of smoke. We found that the area covered is 60,000km 2 and the average concentration was.02g/m 2 –Total mass = 1200 tons The amount produced up to 12pm is only 7% of the amount that was produced the day before

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

16. Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer 2000 - 2003 20 Percentile 98 Percentile90 Percentile 60 Percentile Smoke Sources 98 Percentile

17. Summer AOT 60 Percentile 2000-2004 SeaWiFS AOT, 1 km Resolution Mountain – Low AOT Valley – High AOT Atlanta Birmingham Cloud Contamination?

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.