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Found 15 papers, 10 papers with code
Probabilistic measures afford fair comparisons of AIWP and NWP model output
Then we find PC as the mean continuous ranked probability score (CRPS) of the postprocessed probabilistic forecasts.
Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods
The first approach derives a distribution-based representation of an input ensemble by applying standard dimensionality reduction techniques in a member-by-member fashion and merging the member representations into a joint parametric distribution model.
Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing.
Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
In a case study based on a benchmark dataset for the Jacumba solar plant in the U. S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance and solar power generation.
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.
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.
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.
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
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.
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
