A New Shape Matching-Based Verification Approach for QPFs

Abstract

In this paper, a novel systematic method on the evaluation of quantitative precipitation forecast (QPF) errors from the perspective of rain-area shape verification is proposed. The method aims to improve the accuracy and efficiency of conventional station-based verifications (i.e., standard skill scores), which are insensitive to the biases of station location and rain-area shape and tend to ignore the continuity of precipitation in time and space. The method develops and combines the shape verification indexes, which include the overlap ratio of a forecasted rain-area (Ratiof), the overlap ratio of the ground rainarea (Ratiot), the Jaccard similarity coefficient between the shape of the QPF area, the shape of the ground rain-area (Jaccardshape), the critical success index (CSI) for the rain-area shape (CSIshape), the probability of detection (POD) for the rain-area shape (PODshape), and the false alarm ratio (FAR) for the rain-area shape (FARshape). This definition of QPF verification is applied to a rain event from 2016/08/02 00:30 to 2016/08/02 03:24 in the Guangdong Province. The decomposition of QPF errors into station-based errors and shape error components provides powerful insight into the effects of overall forecasting performance. The experimental results of this investigation show that the proposed method provides an opportunity to assess QPFs objectively and further promote advanced forecasting technologies from this perspective.

Keywords

• Quantitative precipitation forecast (QPF)
• rainfall shape verification
• shape matching
• QPF error