Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Network

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1905.08700.pdf (693.91 KB)

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https://arxiv.org/abs/1905.08700

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http://hdl.handle.net/11603/15021

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UMBC Joint Center for Earth Systems Technology (JCET)
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2019-05-21

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journal article preprints
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Citation of Original Publication

Tianle 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/1905.08700

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Subjects

artificial intelligence-based cloud distributor (AI-CD)
conditional generative adversarial net (cGAN)
MODerate resolution Imaging Spectrometer (MODIS)

Abstract

Here 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.