Improving numerical forecast of the rainstorms induced by Mongolia cold vortex in North China with the frequency matching method

Abstract

Mongolia cold vortex is an important synoptic system causing summer convective precipitation in North China and the numerical model shows poor performances for forecasting the convective rainstorms. It is, therefore, urgent to improve the forecast skill of the rainstorms induced by Mongolia cold vortex. Taking the Haihe River Basin as an example and using the observational precipitation data monitored by automatic meteorological stations and the forecast precipitation data from European Centre for Medium-Range Weather Forecasts (ECMWF), this study is the first time to recombine the numerical forecast series that have the greatest similarity to the observed precipitation by using correlation analysis on a sliding time window. We then corrected the recombinant forecast series by using frequency matching method (FMM) and finally established a correction model for improving precipitation forecast accuracy. Results revealed that FMM could improve the precipitation forecast skill for 1-3 d lead-time. Compared with light, moderate, and heavy rain, the improvement effects are more obvious for rainstorm (50–99.9 mm) and heavy rainstorm (≥100 mm). Moreover, the calibrated forecasts have good performances to improve the accuracy for most magnitudes but not spatial distributions. These findings demonstrate that FMM has good performance for correcting precipitation forecasts induced by Mongolia Cold Vortex and thus can be used to improve the forecast skill of summer rainstorms, providing more accurate forcing inputs for hydrologic model to improve flooding forecast skill in North China.