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. The second part is the forecast phase, where estimates for a i from the training phase are used to create the superensemble. The performance of the individual models is obtained in the training phase using multiple linear regressions against observed (analysis) fields. The outcome of this regression is the weights assigned to the individual models in the ensemble, which are then passed on to the forecast phase to construct the superensemble forecasts. The temporal model anomalies of the
. The second part is the forecast phase, where estimates for a i from the training phase are used to create the superensemble. The performance of the individual models is obtained in the training phase using multiple linear regressions against observed (analysis) fields. The outcome of this regression is the weights assigned to the individual models in the ensemble, which are then passed on to the forecast phase to construct the superensemble forecasts. The temporal model anomalies of the
of this year was rather arbitrary. One set of forecasts for each of the four member models was made for every initial condition starting at 1200 UTC 1 May to 31 August 2000. Therefore, there were 123 forecast experiments altogether for every member of the ensemble. The length of each forecast was 5 days. Initial conditions were taken from the 40-yr ECMWF Re-Analysis (ERA-40). Weekly mean SSTs from Reynolds and Smith (1994) were prescribed over ocean after interpolation to the model run time
of this year was rather arbitrary. One set of forecasts for each of the four member models was made for every initial condition starting at 1200 UTC 1 May to 31 August 2000. Therefore, there were 123 forecast experiments altogether for every member of the ensemble. The length of each forecast was 5 days. Initial conditions were taken from the 40-yr ECMWF Re-Analysis (ERA-40). Weekly mean SSTs from Reynolds and Smith (1994) were prescribed over ocean after interpolation to the model run time
