Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been providing operational Level 2 (swath-based) aerosol optical depth (AOD) and particle property retrievals. A major, multi-year development effort has led to the release of updated operational MISR Level 2 aerosol and land surface retrieval products. The spatial resolution of the product has been increased to 4.4 km, allowing more detailed characterization of aerosol spatial variability, especially near local sources and in urban areas. This development effort is being leveraged by the Multi-Angle Imager for Aerosols (MAIA) investigation that was selected in 2016 by NASA's Earth Venture Instrument program to improve our understanding of the connections between airborne particulate matter (PM) and human health through satellite observations. The MAIA instrument builds upon and extends the capabilities of MISR by making observations in the ultraviolet through the shortwave infrared, including three polarimetric bands.
A critical piece of the MAIA investigation is transforming the total column aerosol information retrieved from space to estimates of near-surface PM mass. In spite of years of work, including studies that have used MISR aerosol data, there is at present skepticism in the community whether this is possible with sufficient fidelity to be useful for health studies. To address such concerns, we will describe the statistical approach that will be used by the MAIA team to establish the spatio-temporal relationships between MAIA-retrieved aerosols and PM measured by ground-based monitors to develop predictions of PM at km-scale resolution. This effort is not without its challenges, and these will be discussed, along with the results of some initial studies based on ground-based and airborne observations demonstrating and validating the MAIA strategy.
Finally, as the future vision temporally and spatially continuous observations of atmospheric aerosols from a constellation of highly capable geostationary satellites rapidly becomes a reality, we will explore what we have learned from nearly two decades of aerosol retrievals from the MISR and the ground-based Aerosol Robotic Network (AERONET). In particular, we will discuss a new class of retrieval algorithms that rely on changing sun angles to infer information about aerosol composition, and justify its application through examination of the temporal autocorrelation in AOD and aerosol properties observed by AERONET. Such retrievals are similar to what is currently done using MISR, but explicitly include a slowly varying temporal component better constrain aerosol composition. The ability of such retrievals to address observational gaps in the MAIA investigation will also be discussed.
Michael Garay is scientist at the NASA Jet Propulsion Laboratory. He is a member of the Multi-angle Imaging SpectroRadiometer (MISR) science team leading the development and testing of aerosol retrievals, and is a co-investigator on the Multi-Angle Imager for Aerosols (MAIA) proposal to study particulate matter (PM) types from space that was selected as part of NASA's Earth Ventures Instrument program. His research interests include satellite remote sensing of clouds and aerosols, polarized radiative transfer, and machine learning techniques.
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