A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.

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In this paper, Atlantic hurricane forecasts for the year 1999 are addressed. The methodology for these forecasts is called the multimodel superensemble. This statistical method makes use of the real-time forecasts provided by a number of operational and research models to construct the superensemble forecasts. This method divides the forecast time line into two phases: a training phase and a forecast combining phase. The training phase includes an inventory of past applicable hurricane forecasts, each by the multimodels. The model biases of position and intensity errors of past forecasts are summarized via a simple linear multiple regression of these forecasts against the best-observed estimates of position and intensity. These statistics are next passed on to future forecasts of the multimodels in order to forecast the hurricanes of 1999. This method was first tested for the hurricanes of 1998 with considerable success, with some of those results summarized here. Those statistics were refined for the 1999 Atlantic hurricane season. Overall, the main result of the seasonal summary is that the position and intensity errors for the multimodel superensemble are generally less than those of all of the participating models during 1–5-day real-time forecasts. Some of the major storms of the 1999 season, such as Dennis, Floyd, Irene, and Lenny, were extremely well handled by this superensemble approach. The message of this study is that the proposed approach may be a viable way to construct improved real-time forecasts of hurricane positions and intensity.

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