Local Quantitative Precipitation Forecast with Minimal Data Requirement—An Ensemble Approach

Abstract

Abstract Operational weather forecasts are routinely performed at convection-allowing resolutions, and thus these forecasts generate weather features that appear to be realistic. However, at times the comparison of the forecast to observations is less favorable, particularly at grid scales. This lack of skill is partly due to the chaotic system underlying the weather. Another problem is that it is impossible to evaluate the risk of making decisions based on deterministic forecasts. However, running global high-resolution ensembles involves substantial computational resources. A 555-m resolution WRF ensemble based on stochastic perturbations of a deterministic forecast of the North American Mesoscale model was created. Observations are used to constrain the ensemble and improve the skill. This method increases the skill for forecasting 60-h accumulated precipitation in five standard, statistical metrics: bias, false alarm ratio, threat score, probability of detection, and success ratio. Furthermore, the ensemble continuous ranked probability score (CRPS) will be compared to a poor man’s ensemble. The forecast error is generally smaller in more than 70% of the case studies performed when compared to nine deterministic model forecasts. The ensemble-enhanced mesoscale system presented can help to determine the most likely scenario without the significant computational requirement of global ensembles and is expected to be useful when global high-resolution ensembles are not available.