TITLE

Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO

AUTHOR(S)

Coelho, C. A. S.; Pezzulli, S.; Balmaseda, M.; Doblas-Reyes, F. J.; Stephenson, D. B.

PUB. DATE

April 2004

SOURCE

Journal of Climate;Apr2004, Vol. 17 Issue 7, p1504

SOURCE TYPE

Academic Journal

DOC. TYPE

Article

ABSTRACT

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Ni�o-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Ni�o-3.4 index values over the period 1950�2001. Coupled model ensemble forecasts for the period 1987�99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987�99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.

ACCESSION #

12728305

Tags: CALIBRATION;  WEATHER forecasting;  BAYESIAN statistical decision theory;  REGRESSION analysis;  CLIMATOLOGY;  CLIMATIC changes

 

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