Verification of Postprocessed Summer Temperature Forecasts at U.K. Sites Using Observed Climate-Based Thresholds

Abstract

Abstract The full range of ensemble forecast members, expressed in percentile format, can give additional valuable information to users compared to a deterministic solution. This study contains the first rigorous assessment of the operational U.K. site-specific percentile forecast generated by the Met Office. Maximum meteorological summer daytime temperature forecasts issued between 2014 and 2016 are analyzed using the ranked probability score (RPS), ranked probability skill score (RPSS), categorized mean squared error (MSE), quantile skill score (QSS), and relative economic value (REV). Site-specific observed climatology is used to define the temperature threshold for each category (where applicable) thereby ensuring identical categorical event base rates across all 99 sites considered. Forecast ranges between 6 and 120 h are assessed, with the RPS decomposition indicating no perceivable change in the reliability yet an almost linear decrease with forecast range solely due to a near-linear increase in the resolution. Using the categorized MSE (the deterministic equivalent to the RPS), the probabilistic forecast is shown to possess more skill than its deterministic counterpart and the disparity between these scores increases with the forecast range. This finding is reinforced by a REV assessment; this indicates that the economic value associated with the probabilistic envelope is greater than that associated with the deterministic solution at the majority of cost–loss ratios. The QSS appears to be well correlated with the RPSS (rs = 0.852 at T + 24) and identifies the outlying quantiles of the probabilistic forecast as being the least skillful.