Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach

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dc.contributorKokhanovsky, Alexander
dc.contributor.authorNoia, Antonio Di
dc.contributor.authorHasekamp, Otto P.
dc.contributor.authorDiedenhoven, Bastiaan van
dc.contributor.authorZhang, Zhibo
dc.date.accessioned2019-04-17T19:11:26Z
dc.date.available2019-04-17T19:11:26Z
dc.date.issued2019-03-18
dc.description.abstractThis 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 & 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.en_US
dc.description.urihttps://www.atmos-meas-tech.net/12/1697/2019/en_US
dc.format.extent20 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2lnrr-cqo1
dc.identifier.citationAntonio Di Noia, et.al, Neural network cloud retrievals from POLDER, AMT , Articles, Volume 12, issue 3, Atmos. Meas. Tech., 12, 1697-1716, 2019 https://doi.org/10.5194/amt-12-1697-2019en_US
dc.identifier.urihttps://doi.org/10.5194/amt-12-1697-2019
dc.identifier.urihttp://hdl.handle.net/11603/13451
dc.language.isoen_USen_US
dc.publisherCopernicus Publications on behalf of the European Geosciences Unionen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectPolarization and Directionality of Earth's Reflectances-3 (POLDER-3)en_US
dc.subjectPolarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL)en_US
dc.subjectModerate Resolution Imaging Spectroradiometer (MODIS)en_US
dc.subjectcloud optical thickness (COT)en_US
dc.titleRetrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approachen_US
dc.title.alternativeNeural network cloud retrievals from POLDERen_US
dc.typeTexten_US

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