Extended Abstract

A Kalman Filter based QPF calibration systemHongping LAN 1 , Fanyou KONG 2 , Jiajia He 1 and Xunlai CHEN 1Shenzhen Meteorological Bureau 1 ,Center for Analysis and Prediction of Storms, University of Oklahoma, USA 2hp2068@sina.comChina Shenzhen Meteorological Bureau, the Center for Analysis and Pr ediction ofStorms, and the Shenzhen Institute of Advanc ed Technology, in a collaborative effort,developed a realtime radar data assimilation and forecasting system, called HAPS –referring to Hourly Assimilation and Predi ction System. Using WRF-ARW modelingsystem with a grid spacing of 4km, H APS analyzes radar data from seven S-bandDoppler radars in the region every hour and produc es 0-6 h QPF. HAPS QPF haslonger lead time compared to typical nowc asting s ystems, but location and intensityerrors exist. A method is proposed to cali brate HAPS 0-6h QPF using Kalman Filtertechnique and blend with 0-3h nowcasting QPF.Past QPF and corresponding QPE are first matc hed in locations. Intensities of QPF andQPE of the same locations are analyzed to obt ain intensity calibr ation field, which wil lbe applied to future forecasts to form calibrated QPF. Two location matching methods,,object matching and correlation coefficient matching, are tested and the later is selected.The working flow is : The correlation co efficient of the past 2h HAPS QPF andcorresponding QPE is calculated over a 500km by 500km region. If the correlationcoefficient is greater than 0.25, the QPF an d QPE are classified t o have same locationand their intensity difference field, assuming Gaussian noise distribution, is filtered usingKalman F ilter method to have a smoothed ca libration field. Wei ghting factors areapplied to the nowcasting QPF and HAPS QP F to produce intensit y calibrated andnowcasting calibrated QPF. Verification against automatic rain gauge data shows higherETS scores for the calibrated QPF compared to those without calibration.-421-