Abstract
Extreme precipitation events have been extensively applied to the design of social infra structures. Thus, a method to more scientifically estimate the extreme event is required. This paper suggests a method to estimate the extreme precipitation in Korea using a regional climate model. First, several historical extreme events are identified and the most extreme event of Typhoon Rusa (2002) is selected. Second, the selected event is reconstructed through the Weather Research and Forecasting (WRF) model, one of the Regional Climate Models (RCMs). Third, the reconstructed event is maximized by adjusting initial and boundary conditions. Finally, the Probable Maximum Precipitation (PMP) is obtained. The WRF could successfully simulate the observed precipitation in terms of spatial and temporal distribution (R2 = 0.81). The combination of the WRF Single-Moment (WSM 6-class graupel scheme (of microphysics), the Betts-Miller-Janjic scheme (of cumulus parameterization) and the Mellor-Yamada-Janjic Turbulent Kinetic Energy (TKE) scheme (of planetary boundary layer) was determined to be the best combination to reconstruct Typhoon Rusa. The estimated PMP (RCM_PMP) was compared with the existing PMP. The RCM_PMP was generally in good agreement with the PMP. The suggested methodology is expected to provide assessments of the existing PMP and to provide a new alternative for estimating PMP.
Highlights
According to the World Meteorological Organization (WMO), the Probable MaximumPrecipitation (PMP) is the theoretical maximum precipitation for a given duration under modern meteorological conditions [1]
This paper suggests a method to estimate the extreme precipitation in Korea using a regional climate model (RCM)
This study focuses on typhoons in historical extreme events
Summary
Precipitation (PMP) is the theoretical maximum precipitation for a given duration under modern meteorological conditions [1]. The PMP approach has been widely used to estimate extreme precipitation, providing disaster risk management procedures including emergency preparedness. Over the last few decades, estimated PMP values have allowed for the design, operation, and risk assessment of large hydraulic infrastructures, such as dams, levees, and urban drainage [2]. These issues remain difficult to solve because many global disasters are caused by heavy precipitation and floods. PMP estimates have relied on statistical and hydro-meteorological approaches. The analyses that solely rely on historical records and their sample statistics may no
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