Precipitation Calibration Based on the Frequency-Matching Method

Abstract

Abstract A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.