Automated Identification of Enhanced Rainfall Rates Using the Near-Storm Environment for Radar Precipitation Estimates

Reliable and timely flash flood warnings are critically dependent on the accuracy of real-time rainfall estimates. Precipitation is not only the most vital input for basin-scale accumulation algorithms such as the Flash Flood Monitoring and Prediction (FFMP) program used operationally by the U.S. National Weather Service, but it is the primary forcing for hydrologic models at all scales. Quantitative precipitation estimates (QPE) from radar are widely used for such a purpose because of their high spatial and temporal resolution. However, converting the native radar variables into an instantaneous rain rate is fraught with challenges and uncertainties. This study addresses the challenge of identifying environments conducive for tropical rain rates, or rain rates that are enhanced by highly productive warm rain growth processes. Model analysis fields of various thermodynamic and moisture parameters were used as predictors in a decision tree-based ensemble to generate probabilities of warm rain-dominated drop growth. Variable importance analysis from the ensemble training showed that the probability accuracy was most dependent on two parameters in particular: freezing-level height and lapse rates of temperature. The probabilities were used to assign a tropical rain rate for hourly QPE and were evaluated against existing Z-R-based QPE products available to forecasters. The probability-based delineations showed improvement in QPE over the existing methods, but the two predictands tested had varying levels of performance for the storm types evaluated and require further study.