2002 Annual

Tuesday, 15 January 2002: 8:45 AM
Multi-Model Superensemble Forecasts for Weather and Seasonal Climate (Invited Presentation)
T. N. Krishnamurti, Florida State University, Tallahassee, FL; and T. S. V. Vijaya Kumar, D. W. Shin, and E. Williford
In this paper we illustrate the performance of a multi-model ensemble forecast for weather and climate. These model comparisons have included the performance within global weather (NWP), seasonal climate and hurricane track and intensity forecasts.

A superensemble concept utilizes the multi-model forecasts and the observed fields during a pre-forecast training period which are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The superensemble forecasts, generated are examined here. This procedure was deployed for the multi-model forecasts of global weather, seasonal climate and hurricane track and intensity forecasts. For each forecast type we note a somewhat superior performance for the multi-model statistical superensemble compared to the performance of the multi-models and the ensemble mean. Our NWP superensemble includes anomaly correlations of geopotential height at 500 hPa and global precipitation forecasts through day-6 of forecasts, and 6-day wind forecasts at 850 hPa and 200 hPa levels. The present version of the superensemble consists of forecasts from 11 different member models, six of them being run locally with the FSU global spectral model which are physically initialized by the multi-analyses obtained through different rain-rate algorithms of TRMM and SSM/I products. Our climate superensemble includes seasonal forecasts using eight member models. Seasonal and multi-seasonal forecasts demonstrate the same skills over climatology from this approach. A number of other issues related to the errors of climatology, optimum number of models required for superensemble, errors of straightforward ensemble averages and the bias of models are also addressed in this analysis. In all these cases, a number of skill estimates for the member models are compared with those of the ensemble mean and the FSU superensemble.

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