Abstract
Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.
Highlights
Aerosols play a critical role in Earth’s radiative balance by directly scattering and absorbing solar radiation, and indirectly interacting with clouds
In this study we developed and analyzed a data thinning technique to improve performance of aerosol and ocean color retrievals from hyper-angular multi-angle polarimeters (MAPs) by screening and removing problematic measurements affected by cloud and other anomalies
We investigated the impact of the number of viewing angles on retrieval uncertainty for the AirHARP and HARP2 instruments based on synthetic data, finding that a total of 20–30 unique angles across all bands were sufficient to achieve good retrieval performance
Summary
Aerosols play a critical role in Earth’s radiative balance by directly scattering and absorbing solar radiation, and indirectly interacting with clouds. Accurate estimation of the water-leaving signal requires the quantification and removal of the aerosol path radiance and the ocean surface reflectance from the remote sensing measurement (Mobley et al, 2016). To advance both aerosol and ocean color characterization based on MAP measurements, simultaneous multi-parameter retrieval algorithms have been developed over both open and coastal waters (Chowdhary et al, 2005; Hasekamp et al, 2011; Xu et al, 2016; Stamnes et al, 2018; Gao et al, 2018; Fan et al, 2019; Gao et al, 2019, 2021)
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