Show simple item record

dc.contributor.authorYuan, Tianle
dc.date.accessioned2019-10-10T14:36:01Z
dc.date.available2019-10-10T14:36:01Z
dc.date.issued2019-05-21
dc.description.abstractHere we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate two-dimensional (2D) marine low cloud reflectance fields. AI-CD uses a conditional generative adversarial net (cGAN) framework to model distribution of 2-D cloud reflectance in nature as observed by the MODerate resolution Imaging Spectrometer (MODIS). Specifically, the AI-CD models the conditional distribution of cloud reflectance fields given a set of largescale environmental conditions such as instantaneous sea surface temperature, estimated inversion strength, surface wind speed, relative humidity and large-scale subsidence rate together with random noise. We show that AI-CD can not only generate realistic cloudy scenes but also capture known, physical dependence of cloud properties on large-scale variables. AI-CD is stochastic in nature because generated cloud fields are influenced by random noise. Therefore, given a fixed set of large-scale variables, an ensemble of cloud reflectance fields can be generated using AI-CD. We suggest that AI-CD approach can be used as a data driven framework for stochastic cloud parameterization because it can realistically model sub-grid cloud distributions and their sensitivity to meteorological variables.en_US
dc.description.urihttps://arxiv.org/abs/1905.08700en_US
dc.format.extent11 pagesen_US
dc.genrejournal article preprintsen_US
dc.identifierdoi:10.13016/m2pqan-zsbk
dc.identifier.citationTianle Yuan, Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Network, Atmospheric and Oceanic Physics, 2019, https://arxiv.org/abs/1905.08700en_US
dc.identifier.urihttp://hdl.handle.net/11603/15021
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
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.subjectartificial intelligence-based cloud distributor (AI-CD)en_US
dc.subjectconditional generative adversarial net (cGAN)en_US
dc.subjectMODerate resolution Imaging Spectrometer (MODIS)en_US
dc.titleArtificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Networken_US
dc.typeTexten_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record