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

E. M. Robinson and R. B. Husar

Washington University, St. Louis, MO 63130

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 Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures. Satellite-Integral

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

Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.

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.

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

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)

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.

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

April 29, 2000, Day 120

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

April 12, 2003 April 10, 2003 Kansas Smoke

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

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

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

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

Summer AOT 60 Percentile

2000-2004 SeaWiFS AOT, 1 km Resolution

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 derive pattern of dust, smoke, haze by filtering, aggregating and fusing the multidimensional data. The paper shows recent results of aerosol characterization using five years of SeaWiFS-derived data over the US, along with companion surface observations along with surface PM chemical and physical data.