Si-Chee Tsay
NASA/Goddard Space Flight Center
Code 613.2
Greenbelt, MD 20771

E-mail: Si-Chee.Tsay-1@nasa.gov
Phone: 301-614-6188
Fax: 301-614-6307

Support is requested to synergize satellite (Terra, A-Train, NPP/NPOESS) observations, surface (SMART-COMMIT) measurements, light-scattering calculations, and radiative-transfer simulations for in-depth aerosol research. Space-borne remote sensing observations often suffer from contamination due to surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from the atmospheric constituents, the sun, and/or the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft and satellite data sets. To bring closure, satellite remote sensing and surface observations are necessary but incomplete due to the unique properties of their data constituted on either the spatial (snapshot global coverage) or temporal (long-term point sites) dimension only. Comprehensive light-scattering and radiative-transfer modeling are required to bridge the temporal and spatial observations, and to serve as the integrator of our understanding of many physical, optical, and radiative processes occurring in the Earth-atmosphere system. NASA�s current Earth Observing System and the future National Polar-orbiting Operational Environmental Satellite System (NPOESS), with the NPOESS Preparatory Project (NPP) serving as a link, are committed to provide Earth System Data Records suitable for long-term climate studies. We propose to advance and validate the Deep Blue algorithm for aerosol retrievals and applications to the current MODIS, future NPP and eventually NPOESS missions by synergizing satellite/surface observations and light-scattering/radiative-transfer modeling capabilities. Specifically, the proposed objectives are as follows.

1. We will advance the bulk properties of aerosol models involved in the Deep Blue retrieval (and MODIS-operational) libraries by using state-of-the-art light scattering and radiative transfer models. Current aerosol retrieval algorithms are based on lookup tables derived from Lorenz-Mie theory, applicable to light scattering by spheres. As many aerosols (dust particles, in particular) are non-spherical particles, we first consider the nonsphericity of aerosols in generating their single-scattering properties. Next, we include complete polarization configuration (i.e., 4x4 phase matrix and full Stokes parameters), mixing of aerosol types, and aerosol vertical distributions in the forward radiative transfer computations. With these efforts, we expect that the accuracy of aerosol retrievals by the Deep Blue will be substantially improved. Finally, sensitivity study of aerosol libraries on the changes of band response function and the shifts of band center will be carried out for a smooth transition from MODIS (Terra/Aqua) to VIIRS (NPP/NPOESS).
2. We will select a number of MODIS observations, with various surface characteristics and aerosol environments, to validate the advanced version of Deep Blue aerosol retrievals against those obtained from collocated operational sensors aboard Terra and A-Train. Extended efforts will be focused on comparing Deep Blue aerosol retrievals with those acquired by SMART-COMMIT (and/or AERONET) from the past and near-future experiments. The goal is to quantify the uncertainty range of Deep Blue retrievals, which is critical to the discussion of aerosol forcing in climate related study.
3. We will synthesize Deep Blue aerosol properties and Aqua/AIRS atmospheric profiles to simulate the radiance fields at the top of atmosphere, and subsequently to compare with the AIRS level-1b radiance data. Collocated solar and infrared spectra from the SMART spectrometer and interferometer, respectively, will be studied particularly for dust aerosols. This effort includes the development of consistent aerosol radiative properties from visible to infrared spectra that can be quite beneficial to the radiative transfer and remote sensing communities.