A Real-Time Algorithm for Merging Radar QPEs with Rain Gauge Observations and Orographic Precipitation Climatology

Abstract

igh-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity?rain rate (Z?R) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a synergistic approach to QPE by combining radar, rain gauge, and an orographic precipitation climatology. In the merged QPE, radar data depict high-resolution spatial distributions of the precipitation and rain gauges provide accurate precipitation measurements that correct potential biases in the radar QPE. The climatology provides a high-resolution background of the spatial precipitation distribution in the complex terrain where radar coverage is limited or nonexistent. The merging algorithm was tested on heavy precipitation events in different areas of the United States and provided a superior QPE to the individual components. The new QPE algorithm is fully automated and can be easily implemented in an operational system.