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000021347 084__ $$2WoS$$aEnvironmental Sciences
000021347 084__ $$2WoS$$aLimnology
000021347 084__ $$2WoS$$aWater Resources
000021347 1001_ $$0P:(DE-Juel1)VDB85768$$aRings, J.$$b0$$uFZJ
000021347 245__ $$aBayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments
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000021347 440_0 $$05958$$aWater Resources Research$$v48$$x0043-1397
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000021347 500__ $$aJasper 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.
000021347 520__ $$aBayesian 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).
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000021347 7001_ $$0P:(DE-HGF)0$$aVrugt, J.A.$$b1
000021347 7001_ $$0P:(DE-HGF)0$$aSchoups, G.$$b2
000021347 7001_ $$0P:(DE-Juel1)129472$$aHuisman, J.A.$$b3$$uFZJ
000021347 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b4$$uFZJ
000021347 773__ $$0PERI:(DE-600)2029553-4$$a10.1029/2011WR011607$$gVol. 48, p. W05520$$pW05520$$q48<W05520$$tWater resources research$$v48$$x0043-1397$$y2012
000021347 8567_ $$uhttp://dx.doi.org/10.1029/2011WR011607
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