From the Multi-angle Imaging SpectroRadiometer (MISR) to MAGARA (A Multi-Angle Geostationary Aerosol Retrieval Algorithm): Improvements in Multi-Angle, Multi-Spectral Aerosol Remote-Sensing Over the Past Decade and into the Next

Open Access
- Author:
- Limbacher, James Alan
- Graduate Program:
- Meteorology and Atmospheric Science
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- October 06, 2022
- Committee Members:
- Chris Forest, Major Field Member
Eugene Clothiaux, Chair & Dissertation Advisor
George Young, Major Field Member
Tim Kane, Outside Unit & Field Member
Ralph Kahn, Special Member
David Stensrud, Program Head/Chair - Keywords:
- Aerosol
aerosols
MISR
GOES
Geostationary
fire
smoke
magara - Abstract:
- Launched over 20 years ago in December of 1999 onboard NASA’s satellite Terra, the Multi-angle Imaging SpectroRadiometer (MISR) has since orbited the Earth over 120,000 times. This imager continues to provide researchers ~400 km wide swaths of the Earth with 9 onboard cameras that measure reflected solar radiation in 4 spectral bands at ~1.1 km spatial resolution. Utilizing this unique sensor, we developed and validated an aerosol retrieval algorithm that provides information about aerosol particle properties ranging from aerosol amount to nonspherical dust loading to fine- and coarse-mode aerosol particle sizes to single-scattering albedos. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere, with its data used for a variety of tasks ranging from weather forecasting to wildfire detection. Located on the GOES-16 and GOES-17 platforms, the Advanced Baseline Imager (ABI) is the first GOES-series imager that meets the precision requirements (e.g., greater than or equal to 10 bits per datum) for high-quality, aerosol-related research. As part of this dissertation, I developed a Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that leverages the two existing ABI sensors on GOES-16 and GOES-17. The algorithm retrieves aerosol loading and aerosol particle properties under a framework that combines the unique information content in multi-angle radiances, such as from MISR, with the robust surface characterization inherent to temporally-tiled algorithms, such as the Multi-Angle Implementation of Atmospheric Correction (MAIAC) method. In addition to demonstrating retrievals of aerosol loading and aerosol particle properties by comparing our retrievals to expectation over biomass-burning regions and dust-plumes, we also validated our results against the AErosol RObotic NETwork (AERONET), a ground-based network of highly accurate sun-viewing photometers. Retrieving aerosol type and aerosol loading on time scales as short as 10 minutes will allow for novel research into aerosol-cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.