Article ID: 2019-066
This paper proposes a new verification metric that can evaluate location errors and shapes of rainfall areas simultaneously: the Pattern Similarity Index (PSI). Pixel-by-pixel verification methods such as the threat score and root mean squared error have difficulties in evaluating location errors and shapes of rainfall areas, and in evaluating small rainfall areas. To address these difficulties, various object-based methods have been developed. However, object-based methods tend to be complicated and computationally expensive. Therefore, PSI adopts a simpler, computationally more efficient algorithm as follows. First, bounding rectangles of individual rainfall areas are computed, and neighboring rectangles are combined so that they are treated as a single precipitation system to mimic the human recognition. Next, shape parameters are computed for each integrated bounding rectangle. For each pair of the observed and forecasted rainfall areas, the location error weighted by the differences of the shape parameters is used as the verification score. If no observed rainfall area with a similar size exists near a forecasted rainfall area, this distance- based score of the forecasted area is set to a large value. The integration method of the bounding rectangle and the precipitation threshold are the only tunable parameters in this method, and we repeat computing the verification score by varying these parameters. The best value is used as the final verification score.Idealized cases showed the ability of PSI to evaluate location errors and differences in the shape parameters. A real case with global precipitation nowcasting showed that the proposed evaluation value increased almost linearly with the forecast time, whereas the threat score and root mean squared error tended to saturate as the forecast time increases, showing a potential advantage of PSI. Comparison with another object-based method revealed the advantage of PSI in its computational efficiency while providing similar verification scores.