Journal Article

PreJuSER-21347

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

Rings, J.FZJ* ; Vrugt, J. A. ; Schoups, G. ; Huisman, J. A.FZJ* ; Vereecken, H.FZJ*

2012
AGU Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/20539  doi:10.1029/2011WR011607

Abstract: Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf).

Keyword(s): J

Note: Jasper A. Vrugt would like to acknowledge financial support from the LDRD project "Multilevel Adaptive Sampling for Multiscale Inverse Problems'' of the Los Alamos National Laboratory.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. Terrestrische Umwelt (P24)

Appears in the scientific report 2012

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Database coverage:
; ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Current Contents - Social and Behavioral Sciences ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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