# Generative modeling of spatio-temporal weather patterns with extreme event conditioning

arXiv: https://arxiv.org/abs/2104.12469 · HTML: https://mlsanity.com/paper/2104.12469/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Lip Movements Generation at a Glance](https://mlsanity.com/paper/1803.10404/) (2018)
- [Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks](https://mlsanity.com/paper/1709.07592/) (2017)
- [COT-GAN: Generating Sequential Data via Causal Optimal Transport](https://mlsanity.com/paper/2006.08571/) (2020)
- [Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery](https://mlsanity.com/paper/2009.07683/) (2020)
- [Physics-informed semantic inpainting: Application to geostatistical modeling](https://mlsanity.com/paper/1909.09459/) (2019)
- [Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting](https://mlsanity.com/paper/1811.06341/) (2018)

## Built on by (most cited first, 6 of 6)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss](https://mlsanity.com/paper/2109.15044/) (2021)
- [COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence](https://mlsanity.com/paper/2205.01224/) (2022)
- [Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events](https://mlsanity.com/paper/2506.06380/) (2025)
- [Inductive biases in deep learning models for weather prediction](https://mlsanity.com/paper/2304.04664/) (2023)
- [Controlling Weather Field Synthesis Using Variational Autoencoders](https://mlsanity.com/paper/2108.00048/) (2021)

## Abstract (arXiv)

Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data.