# Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2002.07579 · HTML: https://mlsanity.com/paper/2002.07579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks](https://mlsanity.com/paper/2105.06386/) (2021)
- [Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding](https://mlsanity.com/paper/2508.17205/) (2025)

## Abstract (arXiv)

We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.