The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context
Problem
Traditional numerical weather prediction is so computationally costly that improvements via higher resolution or larger ensembles are hindered.
Why it matters
Accurate, cheaper forecasts improve weather warnings that protect lives, property, and economies.
What the AI did
Machine learning models trained on ERA5 reanalysis generate forecasts at much lower computational cost; this study provides a first statistical comparison of ML versus NWP forecasts in an operational-like context.
Results
Abstract states ML forecasts are 'highly competitive in terms of accuracy' at much lower computational cost; no specific benchmark numbers in abstract.
Impact
Provides first operational-like statistical evidence that ML forecasts can rival NWP at far lower cost.
Record
| Venue | Bulletin of the American Meteorological Society |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Zied Ben Bouallègue, Mariana Clare, Linus Magnusson, Estíbaliz Gascón, Michael Maier‐Gerber, Martin Janoušek, Mark John Rodwell, Florian Pinault et al. |
| Citations | 211 (OpenAlex, at import time) |
| References | 37 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1175/bams-d-23-0162.1 |
| Open access | bronze |
| Keywords | machine learning, weather forecasting, numerical weather prediction, ERA5, forecast verification |
Importance rating
significant — thousands to millions affected
Abstract states NWP computational cost greatly hinders resolution and ensemble improvements and that ML forecasts are highly competitive at much lower cost; first operational-like comparison, significant but no human-impact scale given.
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