Comparison of rainfall nowcasting derived from the STEPS model and JMA precipitation nowcasts

Shakti P.C., Ryohei Misumi, Tsuyoshi Nakatani, Koyuru Iwanami, Masayuki Maki, Alan W. Seed, Kohin Hirano
Released: October 27, 2015

Comparison of rainfall nowcasting derived from the STEPS model and JMA precipitation nowcasts

Shakti P.C.1), Ryohei Misumi1), Tsuyoshi Nakatani1), Koyuru Iwanami1), Masayuki Maki2), Alan W. Seed3), Kohin Hirano1)

1) Storm, Flood and Landslide Research Unit, National Research Institute for Earth Science and Disaster Prevention
2) Education and Research Center for Regional Disaster Prevention, Kagoshima University
3) Centre for Australian Weather and Climate Research, Bureau of Meteorology, Australia

Quantitative precipitation estimation and precipitation nowcasting are important components of systems that aim at minimizing or managing flash flooding. This study used the Short Term Ensemble Prediction System (STEPS), one of the most advanced Quantitative Precipitation Forecast (QPF) systems currently available. The Japan Meteorological Agency (JMA) radar rainfall data (1-km resolution) from the Kanto region, Japan, covering various periods, were used in STEPS to generate ensemble nowcasts of rainfall. Hour-long 30-member-ensemble rainfall nowcasts were generated for five separate rainfall events using 5-minute time steps. The ensemble nowcasts were verified using radar rainfall data, and the results showed that the STEPS forecasts are in good agreement with the observed data for forecast periods of <1 hour. To check the performance of the STEPS model output in more detail, it was compared with JMA precipitation nowcast data, and both nowcasting datasets were also compared separately with rain gauge data. The skill scores suggest that STEPS generates more accurate nowcasts, especially for higher-intensity rainfall events. Combining all members of the STEPS nowcasting results appears to improve the reliability of short-term rainfall prediction, and the output of such ensemble nowcasts could be used in hydrological models to generate probabilistic forecasts in the future.

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Copyright (c) 2015 Japan Society of Hydrology and Water Resources

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