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N. Vigaud, M. K. Tippett, and A. W. Robertson

Abstract

The skill of submonthly forecasts of rainfall over the East Africa–West Asia sector is examined for starts during the extended boreal winter season (September–April) using three ensemble prediction systems (EPSs) from the Subseasonal-to-Seasonal (S2S) project. Forecasts of tercile category probabilities over the common period 1999–2010 are constructed using extended logistic regression (ELR), and a multimodel forecast is formed by averaging individual model probabilities. The calibration of each model separately produces reliable probabilistic weekly forecasts, but these lack sharpness beyond a week lead time. Multimodel ensembling generally improves skill by removing negative skill scores present in individual models. In addition, the multimodel ensemble week-3–4 forecasts have a higher ranked probability skill score and reliability compared to week-3 or week-4 forecasts for starts in February–April, while skill gain is less pronounced for other seasons. During the 1999–2010 period, skill over continental subregions is the highest for starts in February–April and for starts during El Niño conditions and MJO phase 7, which coincides with enhanced forecast probabilities of above-normal rainfall. Overall, these results indicate notable opportunities for the application of skillful subseasonal predictions over the East Africa–West Asia sector during the extended boreal winter season.

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N. Vigaud, M. K. Tippett, J. Yuan, A. W. Robertson, and N. Acharya

Abstract

The skill of surface temperature forecasts up to 4 weeks ahead is examined for weekly tercile category probabilities constructed using extended logistic regression (ELR) applied to three ensemble prediction systems (EPSs) from the Subseasonal-to-Seasonal (S2S) project (ECMWF, NCEP, and CMA), which are verified over the common period 1999–2010 and averaged with equal weighting to form a multimodel ensemble (MME). Over North America, the resulting forecasts are characterized by good reliability and varying degrees of sharpness. Skill decreases after two weeks and from winter to summer. Multimodel ensembling damps negative skill that is present in individual forecast systems, but overall, does not lead to substantial skill improvement compared to the best (ECMWF) model. Spatial pattern correction is implemented by projecting the ensemble mean temperatures neighboring each grid point onto Laplacian eigenfunctions, and then using those amplitudes as new predictors in the ELR. Forecasts and skill improve beyond week 2, when the ELR model is trained on spatially averaged temperature (i.e., the amplitude of the first Laplacian eigenfunction) rather than the gridpoint ensemble mean, but not at shorter leads. Forecasts are degraded when adding more Laplacian eigenfunctions that encode additional spatial details as predictors, likely due to the short reforecast sample size. Forecast skill variations with ENSO are limited, but MJO relationships are more pronounced, with the highest skill during MJO phase 3 up to week 3, coinciding with enhanced forecast probabilities of above-normal temperatures in winter.

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