Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall

Forecasting rainfall at the seasonal time scale is highly challenging. Seasonal rainfall forecasts are typically made using statistical or dynamical models. The two types of models have different strengths, and their combination has the potential to increase forecast skill. In this study, statistica...

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Bibliographic Details
Published in:Journal of Geophysical Research: Atmospheres 2012-10, Vol.117 (D20), p.n/a
Main Authors: Schepen, Andrew, Wang, Q. J., Robertson, David E.
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian model averaging
Climate
Earth sciences
Earth, ocean, space
Exact sciences and technology
forecast calibration
forecast merging
Forecasting
General circulation models
Geophysics
Hydrology
Mathematics
Precipitation
Probability
Rain
seasonal rainfall forecasting
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Summary:Forecasting rainfall at the seasonal time scale is highly challenging. Seasonal rainfall forecasts are typically made using statistical or dynamical models. The two types of models have different strengths, and their combination has the potential to increase forecast skill. In this study, statistical‐dynamical forecasts of Australian seasonal rainfall are assessed. Statistical rainfall forecasts are made based on observed relationships with lagged climate indices. Dynamical forecasts are made by calibrating raw outputs from multiple general circulation models. The statistical and dynamical forecasts are then merged using a Bayesian model averaging (BMA) method. The skill and reliability of the forecasts is assessed through cross‐validation for the period 1980–2010. We confirm that the dynamical and statistical groups of models give skill in different locations and seasons and the merged statistical‐dynamical forecasts represent a significant improvement in terms of maximizing spatial and temporal coverage of skillfulness. We find that the merged statistical‐dynamical forecasts are reliable in representing forecast uncertainty. Key Points Statistical and dynamical seasonal rainfall forecasting models have unique skill Statistical and dynamical models are merged through Bayesian model averaging Forecast merging improves spatial and temporal coverage of skillfulness
ISSN:0148-0227
2169-897X
2156-2202
2169-8996
DOI:10.1029/2012JD018011