# Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models

arXiv: https://arxiv.org/abs/2504.00128 · HTML: https://mlsanity.com/paper/2504.00128/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 31 Mar 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison](https://mlsanity.com/paper/2106.09512/) (2021)
- [FuXi-2.0: Advancing machine learning weather forecasting model for practical applications](https://mlsanity.com/paper/2409.07188/) (2024)
- [evgam: An R package for Generalized Additive Extreme Value Models](https://mlsanity.com/paper/2003.04067/) (2020)
- [Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models](https://mlsanity.com/paper/2406.09474/) (2024)
- [A comparative review of generalizations of the Gumbel extreme value distribution with an application to wind speed data](https://mlsanity.com/paper/1502.02708/) (2015)

## Built on by (most cited first, 1 of 1)

- [FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting](https://mlsanity.com/paper/2512.08974/) (2025)

## Abstract (arXiv)

Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25deg global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust distributions at specific locations. With the Pangu-Weather NWM, we apply a hierarchy of statistical and deep learning post-processing methods to forecast hourly wind gusts up to three days ahead. To ensure statistical robustness, we constrain our probabilistic forecasts using generalised extreme-value distributions across five regions in Switzerland. Using a convolutional neural network to post-process the predicted atmospheric environment's spatial patterns yields the best results, outperforming direct forecasting approaches across lead times and wind gust speeds. Our results confirm the added value of NWMs for extreme wind forecasting, especially for designing more responsive early-warning systems.