1. Draft proposal to NASA Project SIMBIOS (Jan 19, 2000) Physically-Based Aerosol Models for Atmospheric Correction Algorithms Washington University, St. Louis, MO Center for Air Pollution Impact and Trend Analysis (CAPITA).

2. Table of Contents 1 2 3 4

3. Abstract The objective of the project is to improve the quality of atmospheric correction algorithms by applying physically based aerosol models, multi-sensory data fusion and signal decomposition procedures. The aerosol is composed of multiple aerosol layers, each consisting of a specific aerosol type such as windblown dust, biomass smoke, sea salt, volcanic, biogenic and urban-industrial haze aerosols. Each aerosol type tends to occur over specific global regions and atmospheric strata. Algorithms will be developed to retrieve the spatio-temporal pattern of each aerosol type. The available optical properties (spectral extinction, backscattering and single scatter albedo) as well as the dependence on ambient weather parameters (e.g. humidity) will be updated and extended. A key challenge will be to decompose the total reflectance signal into aerosol and surface components and subsequently separating the contribution of different aerosol types. The apportionment of the total aerosol signal into dust, smoke, salt, volcanic, biogenic and industrial haze will be based on the combination of aerosol climatological data and instantaneous satellite and in-situ observations. Surface-based monitoring (e.g. AERONET, size distribution and chemical analysis) will provide the detailed aerosol characteristics. Satellite data from SeaWiFS, POLDER, MODIS, MISR, TOMS, GOES/GMS/METEOSAT etc will provide the spatial pattern as well as the spectral, angular backscattering and polarization parameters. The multi-sensory data fusion will use a physical model for aerosol size distribution dynamics, a chemical composition model for refractive index and Mie theory for optical properties. The benefits of the new aerosol models, aerosol data fusion and signal decomposition methodology will include (1) Improved aerosol correction by physically and geographically constraining the aerosol selection procedure, () Delivering separate aerosol products for dust, smoke, salt, volcanic, biogenic and haze that will be useful for climate change biogeochemical and other studies and (3) Extendibility through multi-sensory fusion and multi-institutional collaboration.

4. Background Aerosols are a nuisance to satellite remote sensing for surface retrieval –Broad-band interference throughout the spectrum –Variable optical properties dependent on aerosol types and environment therefore it is difficult to accurately account for aerosol interference –Space-time variation due to sources, transport, transformation processes The problem is separating the surface and atmospheric signals –Proper separation yields an undistorted surface color –It also provides an atmospheric aerosol signal useful for other studies Past atmospheric corrections were hampered by inadequate data –No aerosol spectral-angular scattering and absorption properties. No global distribution, No temporal covarage –Atmospheric correction was restricted to data obtained by that particular remote sensor, –Single sensor in inherently limited not enough on spectral angular and absorption characteristics of aerosol –Limited auxiliary data and virtually no data fusion

5. Opportunities Global satellite remote sensors covering ocean and land Aerosol properties from many regional fields studies Technological advances that facilitated multi- sensory data exchange, integration and fusion

6. Aerosol Detection over Land and The Oceans Ocean color is particularly interesting near the continental shores where much of the biological activity is takes place. Most of the atmospheric aerosols are generated over land. Even over the oceans most of the radiative perturbation is due to aerosols from the continents. The new ocean color sensors allow remote sensing of both oceans and land.

7. The Objectives of the Project: Integrate the communal knowledge on the properties and environmental behavior of the major aerosol types that perturb satellite remote sensing of surface color: windblown dust, biomass smoke, sea salt, volcanic, biogenic and urban-industrial haze aerosols Formulate and test algorithms on data fusion and decomposition procedures for the separate retrieval of each of the above aerosol types using data from multiple satellite and surface sensors Deliver the new aerosol retrieval algorithms to the SIMBIOS and broader community for incorporation into atmospheric correction algorithms on operational and research level.

8. Expected Significance of the Project: The project is expected to improve the quality of aerosol correction algorithms by better characterization of the aerosol as a multi-component, dynamic physical system The project will facilitate separate retrieval of dust, smoke, sea salt, volcanic, biogenic and industrial haze aerosol products which will be useful for global climate change, biogeochemical and other studies. The approach is based on physical principles and it can evolve into an integrated data-model assimilation procedure for atmospheric aerosols. The resulting aerosol models will evolve through open consensus-based approach using the Internet and will incorporate the experience of both the atmospheric aerosol and the atmospheric correction research communities. The disadvantages of the proposed approach include (1) more demand on aerosol and environmental input data, (2) more intense computation and (3) several years needed before it will be fully operational

9. Past Aerosol Models and Atmospheric Correction Past Aerosol Models Junge, 1963 – power law Whitby and Husar 1972 – bimodal Shettle and Fenn, 1978 – bimodal, RH, components D’Almeida and Koepke 1980 –components, spatial Past Aerosol Models for Atmospheric Correction CZCS, SeaWiFS - Gordon, Wang et al AVHRR – Stowe, Ignatov, Durkee POLDER – Leroy, Tanre, Vermote MODIS – King, Kaufman et al

10. Recent Work Related to this Project Global Oceanic Aerosol based on AVHRR Global Continental Aerosol Based on Surface Visibility Asian Dust Size Distribution by Different MethodsHaze Retrieval over Ocean and Land from SeaWiFS

11. Approach to the Aerosol Model Development and Implementation Procedures 1.Assume that the vertical aerosol at any give point is the sum of windblown dust, biomass smoke, sea salt, volcanic, biogenic and urban-industrial haze 2.Gather the best available knowledge on the general physico-optical properties of each aerosol type including dependence on humidity, atmospheric residence time,.. 3.Regionalize and seasonalize each aerosol type 4.Define properties for each regional/seasonal aerosol type by constraining its functional form. Some loose scaling factors are included to “fine tune” the aerosol type model. 5.Gather and integrate instantaneous aerosol data from space and surface sensors to determine the model scaling constants 6.Statistically fit the remaining unknown factors to determine the appropriate form for each aerosol type model 7.Apply the appropriate optical model to each aerosol type and calculate aerosol optical parameters relevant to atmospheric correction. 8.Deliver ‘best estimate’ aerosol-optical properties for atmospheric correction.

12. Vertical Pattern of Global Aerosol Windblown Dust (crustal elements) Biomass Smoke (organics, H 2 0 ) Sea H 2 0 salt (NaCl. H 2 0) Stratospheric (Volcanic) (H2SO4) Biogenic (Non-sea salt sulfate, org) Urban-Industrial Haze (SO4, org. H 2 0) Dust, smoke, volcanic aerosol and industrial haze originate from land The global aerosol concentration is highest over land and near the continents over the oceans (coastal regions) Sea salt is significant over some of the windy oceanic regions and biogenic sulfate and organic aerosols also occur …

13. Regional Aerosol Studies: ACE Australia, ACE Africa, SAFARI, SCAR-B ACE - Australia ACE - W. Africa SCAR – Brazil SAFARI – S. Africa

14. Windblown Dust Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

15. Biomass Smoke Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

16. Sea Salt Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

17. Volcanic Aerosol Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

18. Marine Biogenic Haze Aerosol Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

19. Urban-Industrial Haze Aerosol Properties –Size distribution –Chemical composition and refractive index –Spectral extinction, backscattering and single scatter albedo Variation of properties with environmental conditions –Humidity effect including clouds –Transformation/removal effects (chemical, settling) Spatio-temporal distribution –Climatological/seasonal pattern over the ocean and land –Daily distribution –Vertical distribution

20. Aerosol and Surface Radiative Transfer Apparent reflectance detected by the sensor:

21. Apparent Surface Reflectance, R 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. The reflectance of soil and vegetation at 0.8 um is unchanged by haze aerosols since P~ R 0. At large τ (radiation equilibrium), both dark and bright surfaces asymptotically approach the aerosol reflectance, P R is the perturbed reflectance sensed at the top of the atmosphere The critical parameter whether aerosols will increase or decrease the apparent reflectance, R, is the ratio of aerosol to surface reflectance, P/ R 0

22. Aerosol Effect on Surface Color Aerosols add to the reflectance and sometimes reduce the reflectance of surface objects Aerosols always diminish the contrast between dark a bright surface objects They change the color of surface objects Haze adds a bluish while dust adds yellowish tint to the surface color of surface objects.

23. Role of Dust Aerosol on Reflectance tttt

24. Examples of Aerosol Reflectance Decomposition Sahara – Mediterranean Haze – EUS Smoke – Africa, Asia Dust & Haze – Mediterranean

25. Reflectance Decomposition Reflectance over the Mediterranean R = R dust + R smoke + R salt + R biogenic + R volcanic + R haze

26. Deliverables Updated Aerosol Models (size distr., chem. Comp, opt.) –Windblown dust –Biomass smoke –Sea Salt –Biogenic –Volcanic –Urban-industrial haze Methodologies and Data to Drive each of the Models –Satellite data from SeaWiFS, POLDER, MODIS, MISR –Visibility, AERONET, Mass and chemical conc. –Global model-derived parameters Open Facilities for the Evolution and Use of Aerosol Models –Incorporation into atmospheric correction algorithms –Incorporation into aerosol retrieval algorithms –Interactive web-based community stuff

27. Management Approach to the Project Strong interaction with atmospheric aerosol and atmospheric correction communities Integration and updating the best available knowledge through open, participatory consensus-building process using internet Facilitation of strong international participation with emphasis on local participation There will be a ‘best available’ aerosol model and methodology available to the community after year1