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

TL;DR
This paper introduces AI-CD, a novel AI-based framework using conditional GANs to generate realistic 2D marine low cloud reflectance fields conditioned on environmental factors, capturing physical dependencies and enabling stochastic cloud modeling.
Contribution
The paper presents a new AI-driven method employing cGANs to model and generate realistic, physically consistent low cloud reflectance fields conditioned on large-scale environmental variables.
Findings
AI-CD can generate realistic cloud scenes.
It captures physical dependence of clouds on environmental variables.
It enables stochastic ensemble generation of cloud fields.
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 large-scale 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…
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Taxonomy
TopicsSmart Systems and Machine Learning · Internet of Things and AI · Impact of AI and Big Data on Business and Society
