ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

Abstract

ensembleBMA is a contributed R package for probabilistic forecasting using ensemble post-processing via Bayesian Model Averaging. It provides functions for parameter estimation via the EM algorithm for normal mixture models (appropriate for temperature or pressure) and mixtures of gamma distributions with a point mass at 0 (appropriate for precipitation) from training data. Also included are functions giving quantile forecasts based on these models, as well as for verification. ∗Thanks go to Veronica Berrocal and Patrick Tewson for lending their expertise on a number of important issues, and to Michael Polakowski for his work on an earlier version of the package. We are also indebted to Cliff Mass, Jeff Baars, and Eric Grimit for many helpful discussions and for sharing data. Supported by the DoD Multidisciplinary Research Initiative (MURI) administered by the Office of Naval Research under grant N00014-01-10745 and by the Joint Ensemble Forecast System (JEFS) under University Corporation for Atmospheric Research subcontract S06-47225. 1 Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,