Journal article

Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging

QJ Wang, Andrew Schepen, David E Robertson



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 ..

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University of Melbourne Researchers


Funding Acknowledgements

This work was completed as part of the Water Information Research and Development Alliance (WIRADA), a collaboration between CSIRO and the Bureau of Meteorology to facilitate the transfer of research to operations. The work was also partly funded by the CSIRO OCE Science Leader Scheme. We thank Dr. Eun-Pa Lim for providing the grid coordinates used in the Predictive Ocean Atmosphere Model for Australia (POAMA), Cathy Bowditch for proofreading and editing an early version of the manuscript, and Dr. William Wang for providing valuable comments on the work. We are most grateful to two anonymous reviewers, whose insightful critique and constructive suggestions have led us to improve the paper significantly.