A Flexible Bayesian Approach to Bias Correction of Radar-Derived Precipitation Estimates over Complex Terrain: Model Design and Initial Verification

Abstract

Abstract This study develops a flexible Bayesian technique to quantify uncertainties associated with the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) quantitative precipitation estimation (QPE) products over complex terrain. Radar-only rainfall estimates and rain gauge observations over the Russian River watershed in Northern California are utilized to demonstrate this new bias correction approach. Conventional mean field bias (MFB) and local bias (LB) correction methods are also implemented for comparison purposes. Results show that the proposed Bayesian technique outperforms the conventional MFB and LB correction approaches. The radar QPE performance is dramatically improved after the Bayesian-based bias correction: the root-mean-square error is reduced from 4.2 to 1.71 mm, the normalized mean absolute error is reduced from 64.5% to 24.2%, and the correlation with gauge measurements increases from 0.11 to 0.74. In addition, the terrain impact on radar QPE bias correction performance is investigated. After incorporating the terrain elevation information in the Bayesian framework, the QPE performance is further enhanced. Overall, the QPE performance scores after including the terrain information are improved about 10% relative to those only based on rainfall intensity values.