The global distribution of aerosol concentration, size, and chemical composition around the world and, from there, the influence of aerosols on climate and air quality remain key unanswered questions in atmospheric science. This is traceable to the fact that the satellite measurements required for global coverage depend on complex polarimetric optical instruments capable of sensing reflected solar radiation at several wavelengths and viewing directions. Many instruments (e.g., MODIS) have only spectral capability, and, therefore, they cannot provide accurate retrievals in many important cases (e.g., over land). That is primarily why instruments capable of measuring polarization of reflected sunlight have been launched (e.g., POLDER, POLarization and Directionality of the Earth's Reflectances), and will continue to be. However, the corresponding retrieval algorithms rely on quite complex radiative transfer (RT) codes, and currently are mostly based on look-up-table (LUT) approaches. This technique has an advantage of fast retrievals but the method is not flexible with respect to new insights, e.g., about prior assumptions. It is therefore important to perform the corresponding retrievals relying directly on RT calculations during the inversion process, and not on LUTs. Moreover, it is becoming increasingly clear that current retrievals, all based on 1D RT (assuming horizontal scene uniformity), are vulnerable to 3D RT effects in many familiar situations (e.g., scattering of light from nearby clouds, or radiative adjacency effects due to underlying bright surfaces neighboring a dark pixel under consideration). Consequently, the proposed ISSI team will work on two related fronts of equal importance: (1) the “efficiency” problem: speed up of polarized 1D RT computation, to get beyond LUTs without sacrificing accuracy; (2) the “fidelity” problem: assessment and mitigation of 3D RT effects in aerosol property retrievals based on 1D RT.

We have put together a team that combines complimentary expertise in RT (both 1D and 3D), inverse problem solution, and aerosol remote sensing to address both of the stated problems. In particular, we seek approaches for speeding up RT codes that are useful for addressing the aerosol remote sensing. We plan to test the new methods in computational RT and inversion on POLDER data and compare results with ground-based measurements of aerosol optical thickness. In addition, Multiangle Imaging Spectro-Radiometer (MISR) data will be brought to bear. Although without polarization capability, MISR blazed the path for the growing class of multi-angle instruments. The scattering phase function of the atmospheric aerosol can thus be more tightly constrained from space-based measurements. This improves aerosol property retrieval accuracy over land and enables better discrimination between the major aerosol types. However, the full integration of multi-angle, multi-spectral and polarization techniques with realistic scene representation has yet to be done.


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