Filling gaps over the radar rainfall field

Capability of using the Successive Correction Method (SCM) with a Bayesian spatial model to produce a multi-source rainfall data by combining radar, satellite, rain-gauge and PRISM (Parameter-elevation Regressions on Independent Slopes Model) products over a radar gap in the western United States is evaluated. In mountainous regions, such as the western United States, radars suffer from signal blockage which leads to gaps in the radar rainfall field. Even though satellite rainfall products are available without geographic limitations, their accuracy over land is questionable. Since mountainous regions are hydrologically sensitive areas for prediction, the need for a rainfall product with better accuracy than satellite-based products is critical. Daily and 4 X 4 km satellite, radar, rain-gauge, and monthly PRISM precipitation products were used for this study. Rainfall products from satellite IR based Hydro-Estimator and radar Stage-II are merged using the SCM so that the gap over the radar network could be filled. The satellite-radar product from SCM is further combined with rain-gauge and climatological PRISM precipitation products in a Bayesian spatial model. The satellite-radar-gauge-PRISM combined precipitation product is evaluated using three evaluation criteria: coefficient of correlation, bias and RMSE efficiency. Generated multi-source rainfall product using this method produced a better product than Hydro-Estimator when it is evaluated using independent measurements. The present study implies that using the available radar pixels surrounding the gap area, rain-gauge, PRISM and satellite products, a radar like product is achievable over radar gap areas that benefits and has huge impacts on hydrological simulations and prediction purposes.