DOI: 10.1175/JCLI-D-11-00386.1
Scopus记录号: 2-s2.0-84865792477
论文题名: Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging
作者: Wang Q.J. ; Schepen A. ; Robertson D.E.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 16 起始页码: 5524
结束页码: 5537
语种: 英语
Scopus关键词: Australia
; Bayesian methods
; Bayesian model averaging
; Best model
; Climate index
; Extratropical
; Forecast uncertainty
; Individual models
; Leadtime
; Multiple models
; Predictive performance
; Probabilistic forecasts
; Seasonal forecasting
; Seasonal rainfall
; Spatial coverage
; Stable weight
; Statistical forecasting
; Statistical models
; Bayesian networks
; Climate models
; Mergers and acquisitions
; Merging
; Rain
; Forecasting
; Bayesian analysis
; numerical model
; probability
; rainfall
; seasonality
; uncertainty analysis
; weather forecasting
; Australia
英文摘要: Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for aBMAmethod that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. TheBMAmethod outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52291
Appears in Collections: 气候变化事实与影响
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作者单位: CSIRO Land and Water, Highett, VIC, Australia; Bureau of Meteorology, Brisbane, QLD, Australia
Recommended Citation:
Wang Q.J.,Schepen A.,Robertson D.E.. Merging seasonal rainfall forecasts from multiple statistical models through Bayesian model averaging[J]. Journal of Climate,2012-01-01,25(16)