Error Calibration of Numerical Weather Prediction with Historical Data

Using the method Anomaly Numerical-correction with Observations (ANO) based on historical observation data and anomaly integration, we performed a numerical calibration to the high-resolution simulations with the nonhydrostatic WRF3.5 model. The ERA-interim reanalysis and 0.1-degree rainfall data, which is a blending of the surface AWS rainfall observation and retrieved precipitation from CMORPH data, act as the historical observational series. The impact on numerical prediction of disaster weather, especially persistent heavy rainfall is evaluated. The results of the application of ANO in Sichuan heavy rainfall case in July 2013 illustrate that the correction not only improves atmospheric circulation simulation obviously, but also makes the modeled heavy rainfall more realistic in comparison with the observation. Amelioration of circulation and precipitation forecasting, ETS and TS score of heavy rain and also decrease of precipitation bias is observed. The calibration to high-resolution simulation of case 8-13 July shows obvious improvement to the circulation quantities, where an increase of Anomaly Correlation Coefficient (ACC) of the geopotential height by 7.8% and decrease of Root Mean Square Error (RMSE) by 55.7% in average. Meanwhile, ETS and TS score of precipitation (especially heavy rain and above) are also well corrected. The conclusion is supported with the independent samples of high-resolution numerical results across several years.