An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research & Forecasting (WRF) model users. This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area. The ensemble is created using statistical techniques and some heuristics. An initial sample of 90 scheme combinations was first generated using Latin Hypercube Sampling (LHS). Then, after several rounds of screening, a final ensemble of 40 combinations were chosen. The ensemble forecasts generated for both the training and verification cases using those combinations were evaluated based on several verification metrics, including Threat Score (TS), Brier Score (BS), Relative Operating Curve (ROC) and Ranked Probability Score (RPS). The results show that TS of the final ensemble improved by 9-33% over that of the initial ensemble. The reliability for rain ≤ 10 mm d−1 was improved, but decreased slightly for rain > 10 mm d−1 due to insufficient samples. The resolution remained about the same. The final ensemble forecasts were better than that generated from randomly sampled scheme combinations. Those results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.