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This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. [ABSTRACT FROM AUTHOR]

From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = - 0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process. [ABSTRACT FROM AUTHOR]

The University of Maryland, Baltimore County (UMBC) Hyper-Angular Rainbow Polarimeter (HARP2) will be on board NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for spaceborne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. To this end, a cascading retrieval scheme is implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. Two sets of NN models are used for reflectance and polarization, respectively. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from error propagation and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are associated with a reduced number of available viewing angles, which typically occur near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after the PACE launch. [ABSTRACT FROM AUTHOR]