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Research Article
Improved statistical seasonal forecasts using extended training data
Article first published online: 8 JAN 2008
DOI: 10.1002/joc.1661
Copyright © 2008 Royal Meteorological Society
1097-0088/asset/cover.gif?v=1&s=86bd20c4b9e235b31f03217ea75996a9b5386dc5 "International Journal of Climatology")
Additional Information
How to Cite
Wilks, D. S. (2008), Improved statistical seasonal forecasts using extended training data. Int. J. Climatol., 28: 1589–1598. doi: 10.1002/joc.1661
Publication History
- Issue published online: 19 SEP 2008
- Article first published online: 8 JAN 2008
- Manuscript Accepted: 3 NOV 2007
- Manuscript Revised: 2 NOV 2007
- Manuscript Received: 2 MAY 2007
- Abstract
- References
- Cited By
Keywords:
- seasonal forecasts;
- reconstructed SST;
- canonical correlation analysis;
- maximum covariance analysis
Abstract
Statistical seasonal forecasts for gridded North American temperatures are computed using Pacific sea-surface temperature predictors, comparing long (1880 through most recent year) and short (1950 through most recent year) training samples. Use of the longer training series substantially improves the forecasts in winter, and improves forecasts for the longer lead times in other seasons, even though the older reconstructed predictor fields are based on less complete and presumably less reliable information. Forecasts made using canonical correlation analysis and maximum covariance analysis (MCA) perform similarly overall, although the best forecasts in winter are achieved with MCA forecasts that also include a predictor representing the warming trend in recent years. Copyright © 2008 Royal Meteorological Society