Aerosol products from PACE multi-angle polarimetric observations
Meng
Gao
NASA Goddard Space Flight Center
Talk
(Virtual)
The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, successfully launched on Feb 8, 2024, has been designed with the goal of studying the global ocean and atmosphere, with a focus on improving data records of ocean ecology, biogeochemistry, atmospheric aerosols, and clouds. The mission is equipped with state-of-the-art instruments, including the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer, and two Multi-Angle Polarimeters (MAPs), namely the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). These MAP measurements hold a wealth of information that can be used to improve the retrievals of aerosol, cloud, and surface properties simultaneously.
In this talk, we will discuss the PACE MAP aerosol products, their data format, expected uncertainty and availability. The data products include a comprehensive suite of aerosol and surface properties, such as aerosol complex refractive index, effective radius and variance, aerosol layer height, aerosol optical depth, single scattering albedo, etc. The generation of these products from MAP measurements, however, can be computationally prohibitive, due to the high polarimetric measurement accuracy and large data volumes. To expedite operational data processing, deep neural network based radiative transfer models have been developed and incorporated into the PACE polarimetric retrieval algorithm and data processing system. To understand the quality of the retrieved products, a set of global simulated data for both SPEXone and HARP2 are used in pre-launch algorithm testing. These algorithms have since been applied to the real PACE data, with retrievals validated against in-situ measurements.
In this talk, we will discuss the PACE MAP aerosol products, their data format, expected uncertainty and availability. The data products include a comprehensive suite of aerosol and surface properties, such as aerosol complex refractive index, effective radius and variance, aerosol layer height, aerosol optical depth, single scattering albedo, etc. The generation of these products from MAP measurements, however, can be computationally prohibitive, due to the high polarimetric measurement accuracy and large data volumes. To expedite operational data processing, deep neural network based radiative transfer models have been developed and incorporated into the PACE polarimetric retrieval algorithm and data processing system. To understand the quality of the retrieved products, a set of global simulated data for both SPEXone and HARP2 are used in pre-launch algorithm testing. These algorithms have since been applied to the real PACE data, with retrievals validated against in-situ measurements.
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