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• W2898869634 endingPage "1716" @default.
• W2898869634 startingPage "1697" @default.
• W2898869634 abstract "Abstract. This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument aboard the Polarization &amp; Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2 µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level." @default.
• W2898869634 created "2018-11-09" @default.
• W2898869634 creator A5016607732 @default.
• W2898869634 creator A5023386773 @default.
• W2898869634 creator A5043796621 @default.
• W2898869634 creator A5090697008 @default.
• W2898869634 date "2019-03-18" @default.
• W2898869634 modified "2023-10-14" @default.
• W2898869634 title "Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach" @default.
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• W2898869634 doi "https://doi.org/10.5194/amt-12-1697-2019" @default.
• W2898869634 hasPublicationYear "2019" @default.
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