# Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks

arXiv: https://arxiv.org/abs/2205.09182 · HTML: https://mlsanity.com/paper/2205.09182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 11 of 11)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models](https://mlsanity.com/paper/2102.05107/) (2021)
- [Deep Learning for Post-Processing Ensemble Weather Forecasts](https://mlsanity.com/paper/2005.08748/) (2020)
- [Physics-informed neural networks for the shallow-water equations on the sphere](https://mlsanity.com/paper/2104.00615/) (2021)
- [A generative adversarial network approach to (ensemble) weather prediction](https://mlsanity.com/paper/2006.07718/) (2020)
- [Combining distribution-based neural networks to predict weather forecast probabilities](https://mlsanity.com/paper/2103.14430/) (2021)
- [Predicting Weather Uncertainty with Deep Convnets](https://mlsanity.com/paper/1911.00630/) (2019)

## Built on by (most cited first, 5 of 5)

- [SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models](https://mlsanity.com/paper/2306.14066/) (2023)
- [M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts](https://mlsanity.com/paper/2304.09070/) (2023)
- [Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data](https://mlsanity.com/paper/2412.10450/) (2024)
- [Towards replacing precipitation ensemble predictions systems using machine learning](https://mlsanity.com/paper/2304.10251/) (2023)
- [Improving trajectory calculations using deep learning inspired single image superresolution](https://mlsanity.com/paper/2206.04015/) (2022)

## Abstract (arXiv)

Ensemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic control forecast. However, ensemble prediction is associated with a high computational cost and often involves statistical post-processing steps to improve its quality. Here we propose to use deep-learning-based algorithms to learn the statistical properties of an ensemble prediction system, the ensemble spread, given only the deterministic control forecast. Thus, once trained, the costly ensemble prediction system will not be needed anymore to obtain future ensemble forecasts, and the statistical properties of the ensemble can be derived from a single deterministic forecast. We adapt the classical pix2pix architecture to a three-dimensional model and also experiment with a shared latent space encoder-decoder model, and train them against several years of operational (ensemble) weather forecasts for the 500 hPa geopotential height. The results demonstrate that the trained models indeed allow obtaining a highly accurate ensemble spread from the control forecast only.