| description abstract | outine verification of deterministic numerical weather prediction (NWP) forecasts from the convection-permitting 4-km (UK4) and near-convection-resolving 1.5-km (UKV) configurations of the Met Office Unified Model (MetUM) has shown that it is hard to consistently demonstrate an improvement in skill from the higher-resolution model, even though subjective comparison suggests that it performs better. In this paper the use of conventional metrics and precise matching (through extracting the nearest grid point to an observing site) of the forecast to conventional synoptic observations in space and time is replaced with the use of inherently probabilistic metrics such as the Brier score, ranked probability, and continuous ranked probability scores applied to neighborhoods of forecast grid points. Three neighborhood sizes were used: ~4, ~12, and ~25 km, which match the sizes of the grid elements currently used operationally. Six surface variables were considered: 2-m temperature, 10-m wind speed, total cloud amount (TCA), cloud-base height (CBH), visibility, and hourly precipitation. Any neighborhood has a positive impact on skill, either in reducing the skill deficit or enhancing the skillfulness over and above the single grid point. This is true for all variables. An optimal neighborhood appears to depend on the variable and threshold. Adopting this probabilistic approach enables easy comparison to future near-convection-resolving ensemble prediction systems (EPS) and also enables the optimization of postprocessing to maximize the skill of forecast products. | |
