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Numerical Prediction of Heavy Rainfall Using Weather and Research Forecasting Model

Author(s): Masato Kita; Yoshihisa Kawahara; Ryota Tsubaki; Tomoki Ushiyama

Linked Author(s): Ryota Tsubaki, Yoshihisa Kawahara

Keywords: Weather and Research Forecasting model; Heavy rainfall; Data assimilation; Local Ensemble Transform Kalman Filter

Abstract: The increase of heavy rainfall events with global climate change highlights the importance of effective schemes to prevent or mitigate the loss of life and property due to inundation and landslides. The advancement in numerical prediction of heavy rainfall is one of the important measures to give suitable forecast and warning for flood prevention and evacuation activities. This study aims at clarifying the accuracy of a numerical model WRF (Weather and Research Forecasting) coupled with a data assimilation method LETKF (Local Ensemble Transform Kalman Filter). We carried out numerical simulation of heavy rainfall in the northern part of Kyushu Island, Japan, which occurred from July in 2012. Comparison between the calculated hourly rainfall and that measured by a radar system shows that the numerical model can reasonably capture the high mixing ratio and the strong southwest wind over the southern part of Kyushu, which transported moisture to cause the heavy rainfall in the northern Kyushu. It also shows that the prediction without data assimilation fails to reproduce the heavy rain and the southwest wind, which demonstrates the effectiveness of LETKF.

DOI:

Year: 2013

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