Development of an algorithm to retrieve maritime aerosol optical properties using an artificial neural network radiative transfer scheme for GOSAT-2/CAI-2

Abstract:

In this study, we developed a fast yet flexible remote sensing algorithm to estimate the maritime aerosol optical properties for the Cloud and Aerosol Imager-2 (CAI-2) onboard the Greenhouse Gases Observing Satellite-2 (GOSAT-2) launched in October 2018. The CAI-2 is the successor of GOSAT/CAI by providing more spectral and finer spatial data. The algorithm uses the optimal estimation approach to simultaneously retrieve aerosol and water substances (SIRAW), combined with an artificial neural network (ANN) solver to perform the radiative transfer calculation. The ANN was well constructed based on an improved learning scheme and educated from a coupled atmosphere-ocean vector radiative transfer model over both open and coastal water. To investigate the availability of SIRAW, the retrieval was conducted using the real CAI-2 data and preliminarily validated via the ground-based observation of Aerosol Robotic Network and Maritime Aerosol Network over different ocean regions from March to November in 2019. Moreover, inter-comparison of aerosol and water substances between CAI-2 and MODIS over the global ocean region was also performed.