Localized heavy rainfall prediction using selective cloud-radar data assimilation based on automated cumulonimbus tracking

Rapidly developing cumulonimbus clouds can cause localized heavy rainfall, leading to significant damage such as sudden rises in river water levels and even loss of human life. Therefore, there is a strong demand for earlier and highly accurate prediction methods. In this study, we developed a selective data assimilation method utilizing ground-based scanning type Ka-band radar (cloud radar), which can detect cloud droplets smaller than raindrops, to predict localized heavy rainfall from the cloud development stage before precipitation starts.

Previous research (Kato et al., 2022, WAF) successfully predicted localized heavy rainfall approximately 20 minutes ahead by assimilating high-frequency (every 1 minute) 3D special observational data obtained from simultaneous sector scanning by three cloud radars into a cloud-resolving numerical model (CReSS) with a horizontal grid spacing of 700 m. However, the assimilation method used (nudging-based humidification of cloud regions) assimilated not only developing clouds but also weakening clouds, resulting in unnecessary humidification and false precipitation forecasts.

To overcome this issue, we propose a new method that selectively assimilates only developing clouds. Specifically, we applied an automatic cumulonimbus cloud tracking algorithm (AITCC) to the cloud radar data, automatically extracting parameters such as cloud area and maximum reflectivity for each cell. By analyzing the temporal changes of these parameters, we could automatically distinguish between rapidly developing clouds and other clouds.

We verified the effectiveness of the proposed method through the special observational case and found that false precipitation forecasts observed without selective assimilation were suppressed. Consequently, the method successfully predicted true localized heavy rainfall accurately. In the future, we plan to further validate this method using multiple cases to achieve rapid and highly accurate predictions of localized heavy rainfall starting from the pre-precipitation stage.