Neural Networks for Postprocessing Ensemble Weather Forecasts
Problem
Ensemble weather forecasts contain systematic errors and need statistical postprocessing to produce reliable probabilistic forecasts, traditionally done with distributional regression and prespecified link functions.
Why it matters
Reliable probabilistic weather forecasts protect lives and support decision-making across weather-sensitive sectors.
What the AI did
A neural network automatically learns nonlinear relationships between arbitrary predictors and forecast distribution parameters, using auxiliary variables and station-specific embeddings.
Results
Significantly outperformed benchmark postprocessing methods while being computationally more affordable in a case study of 2-m temperature forecasts in Germany; no numeric metrics given.
Impact
Peer-reviewed AMS journal article demonstrating a superior postprocessing method in one case study.
Record
| Venue | Monthly Weather Review |
|---|---|
| Published | 2018 |
| Work type | article |
| Authors | Stephan Rasp, Sebastian Lerch |
| Citations | 509 (OpenAlex, at import time) |
| References | 86 |
| Model or method | Neural networks with embeddings (no named model) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1175/mwr-d-18-0187.1 |
| Open access | hybrid |
| Keywords | neural networks, ensemble forecasts, statistical postprocessing, probabilistic forecasting, embeddings |
Importance rating
significant — thousands to millions affected
Reliable probabilistic forecasts aid weather-sensitive decisions; abstract claims method could transform forecast postprocessing but gives no scale of beneficiaries or lives affected.
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