Object‐based verification metrics applied to the evaluation and weighting of convective‐scale precipitation forecasts

Abstract

Traditional pointwise verification scores are not always appropriate for the evaluation of high‐resolution precipitation forecasts because of double‐penalty problems. An alternative approach, based on the identification of homogeneous rainfall areas called “precipitating objects”, allows forecast evaluation at a larger and thus more predictable scale, and specific information about the nature of errors (e.g. location, size, intensity) can be obtained. A novel object detection method is first introduced and the object‐based verification of precipitation forecasts from the convective‐scale deterministic and ensemble models Arome and Arome‐EPS is then discussed, using several scores and diagnostics. Three types of precipitating objects characterizing total, moderate and heavy rainfall are considered. In the second part, object‐based metrics are used to compute objective weights for time‐lagged ensemble forecasts, based on their performance at early forecast ranges. The weights obtained clearly depend on the meteorological situation and on the precipitation type, reflecting for instance the lower predictability of moderate precipitation compared to total precipitation. There is also a dependence on the production time with, on average, slightly larger and more homogeneous weights associated with the most recent run. However, in some situations of moderate and heavy rainfall, a relevant signal can be extracted from older runs. It is finally shown that object‐based weights are better suited than classical quadratic weights to improve nowcasting performance.