Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

Advanced inversion Multi-term approach utilizing multiple a priori constraints is proposed. The approach is used as a base for the first unified algorithm GRASP that is applicable to diverse remote sensing observations and retrieving a variety of atmospheric properties. The utilization of GRASP for diverse remote sensing observations is demonstrated.

Aerosol models, composed of size distribution, complex refractive index, and spherical fraction, are derived from a new synergistic retrieval of airborne in situ angular scattering measurements made by the Polarized Imaging Nephelometer and absorption measurements from the Particle Soot Absorption Photometer. The data utilized include phase function (F11), degree of polarization (−F12/F11), and absorption coefficient (βabs) measured at low relative humidities during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convection Clouds and Chemistry (DC3) field campaigns. The Generalized Retrieval of Aerosol and Surface Properties (GRASP) is applied to these measurements to obtain summaries of particle properties that are optically consistent with the original measurements. A classification scheme is then used to categorize the corresponding retrieval results. Inversions performed on the DC3 measurements indicate the presence of a significant amount of dust‐like aerosol in the inflow of storms sampled during this campaign, with the quantity of dust present depending strongly on the underlying surface features. In the SEAC4RS data, the retrieved size distributions were found to be remarkably similar among a range of aerosol types, including urban and industrial, biogenic, and biomass burning (BB) emissions. These aerosol types were found to have average fine mode volume median radii 0.155 ≤ rvf ≤ 0.163μm and lognormal standard deviations 0.32 ≤ σf ≤ 0.36. There were, however, consistent differences between the angular scattering patterns of the BB samples and the other particle types. The GRASP retrieval predominantly attributed these differences to elevated real and imaginary refractive indices in the BB samples (m532nm≈1.55+0.007i) relative to the two other categories (m532nm≈1.51+0.004i).

Abstract. This work provides a synopsis of aerosol phase function (F11) and polarized phase function (F12) measurements made by the Polarized Imaging Nephelometer (PI-Neph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F11 and -F12/F11 for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular-scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 % recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.