A common problem with ensemble weather forecasts is the insufficient spread of the ensemble members so that often the true state lies outside the span of the ensemble forecasts. Previous studies indicate that this "lack of spread" is worse near the surface than it is in mid-troposphere, suggesting that part of the problem may be due to the land surface conditions in the model. The PIs seek to improve this situation by perturbing the land surface condition and treating the insolation (incoming solar radiation at the surface) stochastically in the ensemble forecasts. The research has two components: first, an observational/diagnostic study geared toward the understanding of the spatial and temporal characteristics of errors in surface fluxes and insolation. The second component is to design methodologies to link the errors of the surface fluxes to the surface moisture and perturb it. The isolation will be perturbed with stochastic noise. Once suitable candidate methods have been developed, they will be tested in the Rapid Update Cycle or Weather Research and Forecast model ensemble runs by the Forecast System Laboratory, NOAA. Results from this research have the potential of finding their way into operational ensemble weather forecasts.
Probabilistic forecasts generated by ensemble predictions are increasing used for a wide variety of purposes, from flood forecasting to assessing the demand for electricity. This research is aimed at improving the quality of ensemble forecasts and enhancing their usefulness. The training that the graduate students receive will help to prepare them for careers in ensemble forecasting where there is a shortage of talented manpower.
