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Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled forlaunch in the timeframe of 2023, will carry a hyperspectral scanningradiometer named the Ocean Color Instrument (OCI) and two multi-anglepolarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and theSRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAPmeasurements contain rich information on the microphysical properties ofaerosols and hydrosols and therefore can be used to retrieve accurate aerosolproperties for complex atmosphere and ocean systems. Most polarimetric aerosolretrieval algorithms utilize vector radiative transfer models iteratively inan optimization approach, which leads to high computational costs that limittheir usage in the operational processing of large data volumes acquired bythe MAP imagers. In this work, we propose a deep neural network (NN) forwardmodel to represent the radiative transfer simulation of coupled atmosphere andocean systems for applications to the HARP2 instrument and itspredecessors. Through the evaluation of synthetic datasets for AirHARP(airborne version of HARP2), the NN model achieves a numerical accuracysmaller than the instrument uncertainties, with a running time of0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as aforward model, we built an efficient joint aerosol and ocean color retrievalalgorithm called FastMAPOL, evolved from the well-validated Multi-AngularPolarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol propertiesand water-leaving signals were conducted on both the synthetic data and theAirHARP field measurements from the Aerosol Characterization from Polarimeterand Lidar (ACEPOL) campaign in 2017. From the validation with the syntheticdata and the collocated High Spectral Resolution Lidar (HSRL) aerosolproducts, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptableerror. Comparing to the retrieval speed using a conventional radiative transferforward model, the computational acceleration is 103 times faster with CPUor 104 times with GPU processors. The FastMAPOL algorithm can be used tooperationally process the large volume of polarimetric data acquired by PACEand other future Earth-observing satellite missions with similar capabilities.

Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model