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Found 8 papers, 5 papers with code
Convolutional autoencoders for spatially-informed ensemble post-processing
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing.
Aggregating distribution forecasts from deep ensembles
The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting.
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.
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
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.
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.
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
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
