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Found 3 papers, 2 papers with code
Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions.
Aggregating distribution forecasts from deep ensembles
Using theoretical arguments and a comprehensive analysis on twelve benchmark data sets, we systematically compare probability- and quantile-based aggregation methods for three neural network-based approaches with different forecast distribution types as output.
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.
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