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Found 11 papers, 7 papers with code
Neural networks for post-processing ensemble weather forecasts
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.
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.
Aggregating distribution forecasts from deep ensembles
The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting.
Generative machine learning methods for multivariate ensemble post-processing
Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts.
Uncertainty quantification for data-driven weather models
In a case study on medium-range forecasts of selected weather variables over Europe, the probabilistic forecasts obtained by using the Pangu-Weather model in concert with uncertainty quantification methods show promising results and provide improvements over ensemble forecasts from the physics-based ensemble weather model of the European Centre for Medium-Range Weather Forecasts for lead times of up to 5 days.
Evaluating probabilistic forecasts with the R package scoringRules
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography.
Computation Applications
Convolutional autoencoders for spatially-informed ensemble post-processing
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing.
Probabilistic Forecasting and Comparative Model Assessment Based on Markov Chain Monte Carlo Output
Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output.
Methodology
Machine learning for total cloud cover prediction
We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor.
From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work
We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting.
Weather Forecasting
Instrumentation and Methods for Astrophysics
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.
