# Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models

arXiv: https://arxiv.org/abs/2406.09474 · HTML: https://mlsanity.com/paper/2406.09474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [Climate-Invariant Machine Learning](https://mlsanity.com/paper/2112.08440/) (2021)
- [Dynamical Tests of a Deep-Learning Weather Prediction Model](https://mlsanity.com/paper/2309.10867/) (2023)
- [Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciar'an](https://mlsanity.com/paper/2312.02658/) (2023)
- [Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications](https://mlsanity.com/paper/2311.13691/) (2023)

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

- [Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather](https://mlsanity.com/paper/2605.01126/) (2026)
- [Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models](https://mlsanity.com/paper/2504.00128/) (2025)

## Abstract (arXiv)

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.