The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Neural Networks for Postprocessing Ensemble Weather Forecasts
Open accessAbstract
Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.
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Deep learning for computational biology
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Could Machine Learning Break the Convection Parameterization Deadlock?
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Cited by
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002203 · Cited by 663
Self-citationNeural general circulation models for weather and climate
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454
“Our stochastic loss is based on the CRPS 28,48,49 .”
MethodSelf-citationWeyn, J. A., Durran, D. R., & Caruana, R. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002109 · Cited by 336
Forecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 280
WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez‐Gonzalez, A., Yang, V., Carver, R., Agrawal, S., et al. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004019 · Cited by 245
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Metadata
- Authors
- Rasp, S., & Lerch, S.
- Venue
- Monthly Weather Review
- Publisher
- American Meteorological Society
- Published
- 2018-11
- Volume
- 146
- Issue
- 11
- Pages
- 3885-3900
- ISSN
- 0027-0644 (ISSN-L)
- 1520-0493
- OpenAlex
- W2803408063
- Cited by
- 509
- References
- 11
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
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