MULTI-SOURCES PRECIPITATION ESTIMATION: MITIGATING GAPS OVER RADAR NETWORK COVERAGE

Precipitation is one major parameter for various applications ranging from short term hydrological to long term climate studies. Rainfall measurements from ground-based radar networks are the most common precipitation product used as input of hydrologic models for flood forecasting. However, radar coverage itself is limited by different uncertainty sources such as terrain blockage and beam overshooting, even within fairly dense network. In the present study, a two-step approach for merging radar and satellite rainfall products is evaluated to mitigate an artificially created radar gap in Oklahoma. Real gap areas over radar network cannot be used as a test-bed due to lack of availability of radar rainfall required for validation of generated multi-sources product. Hourly satellite IR based Hydro-Estimator (HE) and radar Stage-IV (ST-IV) for the year 2006 are used in this study. The two steps of merging process are: 1) bias correction of the satellite rainfall product against radar rainfall using the method of ensembles; 2) merging the two, radar and satellite, rainfall products using the Successive Correction Method (SCM) and Bayesian to fill the artificially created gap areas over the radar network. The present study implies that merged radar like product is achievable over radar gap areas using the ensemble bias correction and merging approches.