# Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison

arXiv: https://arxiv.org/abs/2106.09512 · HTML: https://mlsanity.com/paper/2106.09512/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 88 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018)
- [Isotonic Distributional Regression](https://mlsanity.com/paper/1909.03725/) (2019)
- [Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling](https://mlsanity.com/paper/1302.7149/) (2013)
- [Deep Distribution Regression](https://mlsanity.com/paper/1903.06023/) (2019)
- [Log-normal distribution based EMOS models for probabilistic wind speed forecasting](https://mlsanity.com/paper/1407.3252/) (2014)
- [Combining predictive distributions for statistical post-processing of ensemble forecasts](https://mlsanity.com/paper/1607.08096/) (2016)
- [Distributional (Single) Index Models](https://mlsanity.com/paper/2006.09219/) (2020)
- [Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts](https://mlsanity.com/paper/2008.11539/) (2020)
- [Calibration of wind speed ensemble forecasts for power generation](https://mlsanity.com/paper/2104.14910/) (2021)

## Built on by (most cited first, 30 of 34)

- [Generative machine learning methods for multivariate ensemble post-processing](https://mlsanity.com/paper/2211.01345/) (2022)
- [Uncertainty quantification for data-driven weather models](https://mlsanity.com/paper/2403.13458/) (2024)
- [Precipitation nowcasting with generative diffusion models](https://mlsanity.com/paper/2308.06733/) (2023)
- [Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications](https://mlsanity.com/paper/2309.13207/) (2023)
- [An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression](https://mlsanity.com/paper/2504.18433/) (2025)
- [Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models](https://mlsanity.com/paper/2106.13924/) (2021)
- [Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks](https://mlsanity.com/paper/2309.04452/) (2023)
- [Aggregating distribution forecasts from deep ensembles](https://mlsanity.com/paper/2204.02291/) (2022)
- [Convolutional autoencoders for spatially-informed ensemble post-processing](https://mlsanity.com/paper/2204.05102/) (2022)
- [A two-step machine learning approach to statistical post-processing of weather forecasts for power generation](https://mlsanity.com/paper/2207.07589/) (2022)
- [Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts](https://mlsanity.com/paper/2407.11050/) (2024)
- [Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk](https://mlsanity.com/paper/2205.04360/) (2022)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning](https://mlsanity.com/paper/2307.09060/) (2023)
- [Generating ensembles of spatially-coherent in-situ forecasts using flow matching](https://mlsanity.com/paper/2504.03463/) (2025)
- [Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning](https://mlsanity.com/paper/2406.04424/) (2024)
- [Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts](https://mlsanity.com/paper/2406.14159/) (2024)
- [Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts](https://mlsanity.com/paper/2407.02125/) (2024)
- [Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts](https://mlsanity.com/paper/2412.13957/) (2024)
- [Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall](https://mlsanity.com/paper/2504.05471/) (2025)
- [Physics-constrained deep learning postprocessing of temperature and humidity](https://mlsanity.com/paper/2212.04487/) (2022)
- [Ensemble weather forecast post-processing with a flexible probabilistic neural network approach](https://mlsanity.com/paper/2303.17610/) (2023)
- [Statistical post-processing of visibility ensemble forecasts](https://mlsanity.com/paper/2305.15325/) (2023)
- [Machine learning-based probabilistic forecasting of solar irradiance in Chile](https://mlsanity.com/paper/2411.11073/) (2024)
- [Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach](https://mlsanity.com/paper/2502.00300/) (2025)
- [Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods](https://mlsanity.com/paper/2502.04409/) (2025)
- [Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models](https://mlsanity.com/paper/2504.00128/) (2025)
- [Improving Medium Range Severe Weather Prediction through Transformer Post-processing of AI Weather Forecasts](https://mlsanity.com/paper/2505.11750/) (2025)
- [Analogue Forecast System for Daily Precipitation Prediction Using Autoencoder Feature Extraction: Application in Hong Kong](https://mlsanity.com/paper/2501.02814/) (2025)
- [Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation](https://mlsanity.com/paper/2507.16219/) (2025)

## Abstract (arXiv)

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, that can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.