The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer

Abstract

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.

Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models

“…Advanced simultaneous aerosol and surface property retrieval algorithms have been developed for MAP instruments (Chowdhary et al, 2005;Waquet et al, 2009;Hasekamp et al, 2011;Dubovik et al, 2011Dubovik et al, , 2014Wang et al, 2014;Wu et al, 2015;Xu et al, 2016;Fu and Hasekamp, 2018;Li et al, 2018;Stamnes et al, 2018;Gao et al, 2018;Li et al, 2019;Hasekamp et al, 2019b;Chen et al, 2020;Fu et al, 2020;Puthukkudy et al, 2020;Gao et al, 2021a;Xu et al, 2021;Gao et al, 2023;Stamnes et al, 2023). Most of these retrieval algorithms developed for MAP observations are based on iterative optimization approaches that utilize vector radiative transfer (RT) forward models, capable to derive atmospheric and surface properties simultaneously.…”

“…To process HARP2 data efficiently, the FastMAPOL algorithm was developed, powered by such NN-based radiative transfer forward model and validated using AirHARP field campaign measurements (Gao et al, 2021a, b) and HARP2 synthetic data (Gao et al, 2021b(Gao et al, , 2022). Stamnes et al (2023) utilized NN-based forward models that combine spectral bands from both HARP2 and SPEXone in MAP retrievals . These recent developments build upon the successful application of NNs in non-polarimetric remote sensing (Diego and Loyola, 2004;Schroeder et al, 2007;Fan et al, 2017;Chen et al, 2018;Nanda et al, 2019;Shi et al, 2020;Ukkonen, 2022;Stegmann et al, 2022;Ibrahim et al, 2022), and achieve the high radiometric and polarimetric accuracy of modern MAPs by using a larger number of hidden layers (e.g.…”