Bias-corrected short-range ensemble forecasts of near surface variables

Abstract

A multimodel short-range ensemble forecasting system created as part of a National Oceanic and Atmospheric Administration program on improved high temperature forecasting during the summer of 2003 is evaluated. Results from this short-range ensemble system indicate that using the past complete 12 days of forecasts to bias correct today's forecast yields ensemble mean forecasts of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speed that are competitive with or better than those available from any of the model output statistics presently generated operationally in the United States. However, the bias-corrected ensemble system provides more than just the ensemble mean forecast. Probabilities produced by this system are skilful and reliable, and have been found to be valuable when evaluated in a cost-loss model. The ensemble appears to provide better guidance for more unlikely events, such as very warm temperatures, that likely have the greatest economic significance. Industries that are sensitive to the weather, such as power companies, transportation and agriculture, may benefit from the probability information provided. Thus, it is possible to develop post-processing for short-range ensemble forecasting systems that is competitive with or better than traditional post-processing techniques, thereby allowing the rapid production of useful and accurate guidance forecasts of many near surface variables.