Development and Verification of Radar-Satellite Blended QPF to Rainfall Forecasting

Generally, extrapolation based on the motion vector is the best method of precipitation forecasting using radar rainfall data for a time frame within a few hours from the present. However, there is a need for improvement due to the radar rainfall being less accurate than rain-gauge on surface. To improve the radar rainfall and to take advantage of the COMS (Communication, Ocean and Meteorological Satellite) data, a technique to blend the different data types for very short range forecasting purposes was developed in the present study. The motion vector of precipitation systems are estimated using 1.5km CAPPI (Constant Altitude Plan Position Indicator) reflectivity by pattern matching method, which indicates the systems’ direction and speed of movement and blended radar-COMS rain field is used for initial data. The accuracies of rainfall forecasting data were verified utilizing AWS (Automatic Weather System) observed data for an extreme rainfall occurred by typhoon Chaba in the southern part of Korean Peninsula on 5 October 2016. The rainfall prediction results differ with the observed data. However, it is expected that the performance will be improved through various statistical post-processing steps, and it is necessary to improve the algorithm for the uncertainty of rainfall forecasting, parameter optimization, and calibration.