[A-AS03] Extreme Events and Mesoscale Weather: Observations and Modeling

[AAS03-P03] Estimation of the Probable Maximum Precipitation for Mesoscale Convective Systems Using a Numerical Weather Model

*田原 亮太郎¹、平賀 優介¹ (1.国立大学法人東北大学)

キーワード：線状降水帯、Weather Research and Forecasting model

PMP has traditionally been defined as “the greatest depth of precipitation for a given duration meteorologically possible for a design watershed at a particular location at a particular time of year”. Common methods for estimating PMP include the statistical approach and hydrometeorological techniques, which are widely used for the design of dams, nuclear power plants, and other infrastructure. Despite these traditional definitions and estimation methods of PMP being used worldwide, various issues have been pointed out with these traditional definitions and estimation methods of PMP, such as the lack of consideration for physical processes, assumptions like climate stationarity, and others. Therefore, in recent years, the estimation of PMP using numerical weather models has gained attention due to the ability to obtain spatiotemporal rainfall distributions based on physical laws through numerical calculations. Traditional PMP estimation methods using numerical weather models have successfully increased precipitation amounts by maximizing relative humidity in the initial and boundary conditions (RHM: Relative Humidity Maximization), which leads to the maximization of water vapor content. On the other hand, previous studies have shown that applying this conventional method to mesoscale convective systems (MCSs) rainfall events does not necessarily lead to an increase in precipitation amounts, indicating challenges in estimating PMP for MCSs using numerical weather models. This study investigates a new method for estimating PMP using numerical weather models, focusing on rainfall events associated with MCSs.