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

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 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.