Prediction of Meso-γ-Scale Local Heavy Rain by Ground-Based Cloud Radar Assimilation with Water Vapor Nudging

Abstract

Abstract Meso-γ-scale (2–20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud radar data assimilation cycling. Results suggest that NWP with cloud radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations. Significance Statement The development of prediction methods for local (within several kilometers) heavy rain (LHR) is important because LHR events can cause deaths through the sudden rise of rivers and flooding of roads by rapidly developing (≤30 min) rain clouds. This study aims to develop a method for predicting LHR even before it begins to rain, which has been difficult to date. Using a technique called data assimilation, which integrates observation and simulation, we developed a method for assimilating cloud radar observations that can capture cloud droplets before raindrops form. As a result, we succeeded in predicting LHR before rainfall commenced. By extending and applying this research, early evacuation of vulnerable populations during LHR is possible.