Calibration of Ensemble Forecasts Using Reforecast Datasets

Fine-scale, well-calibrated probabilistic weather forecasts are increasingly in demand for such weather-critical applications as battlespace management, and accurate prediction of high-value airborne operations. Built upon the Phase I effort we have just completed, the principal objective of this proposed study (Phase II) is to develop a reliable, efficient, and easily deployable reforecast-calibration system prototype based on fine-scale ensemble reforecast datasets and use it to produce well-calibrated, fine-scale probabilistic weather forecast products. This study consists of three major components. The first component is to produce a basic 20-year fine-scale ensemble reforecast dataset over the CONUS domain using the WRF-ARW modeling system. The second component is to further develop and examine various MOS-based statistical models that apply to more variables and multiple classes and to refine the Phase I KNN calibration techniques, using the large reforecast sample base, and to compare the two techniques. The third component is to conduct sensitivity experiments to study data-sparse impact and the trade-offs between ensemble size and reforecast length, in order to assess the feasibility and effectiveness of the deployable reforecast-calibration system.