This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble postprocessing via Bayesian model averaging (BMA), and ProbForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). The model parameters are estimated from training data. The GOP technique uses geostatistical methods to produce probabilistic forecasts of entire weather fields for temperature or pressure, based on a single numerical forecast on a spatial grid. Both packages include functions for evaluating predictive performance, in addition to model fitting and forecasting.
ensembleBMA, chron, fields, maps, ProbForecastGOP, RandomFields, fields
Spatial, TimeSeries, Bayesian, SpatioTemporal
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For attribution, please cite this work as
Fraley, et al., "The R Journal: Probabilistic Weather Forecasting in R", The R Journal, 2011
BibTeX citation
@article{RJ-2011-009,
author = {Fraley, Chris and Raftery, Adrian and Gneiting, Tilmann and Sloughter, McLean and Berrocal, Veronica},
title = {The R Journal: Probabilistic Weather Forecasting in R},
journal = {The R Journal},
year = {2011},
note = {https://doi.org/10.32614/RJ-2011-009},
doi = {10.32614/RJ-2011-009},
volume = {3},
issue = {1},
issn = {2073-4859},
pages = {55-63}
}