Ensemble versus Deterministic Performance at the Kilometer Scale

Abstract

AbstractWhat is the benefit of a near-convection-resolving ensemble over a near-convection-resolving deterministic forecast? In this paper, a way in which ensemble and deterministic numerical weather prediction (NWP) systems can be compared is demonstrated using a probabilistic verification framework. Three years? worth of raw forecasts from the Met Office Unified Model (UM) 12-member 2.2-km Met Office Global and Regional Ensemble Prediction System (MOGREPS-UK) ensemble and 1.5-km Met Office U.K. variable resolution (UKV) deterministic configuration were compared, utilizing a range of forecast neighborhood sizes centered on surface synoptic observing site locations. Six surface variables were evaluated: temperature, 10-m wind speed, visibility, cloud-base height, total cloud amount, and hourly precipitation. Deterministic forecasts benefit more from the application of neighborhoods, though ensemble forecast skill can also be improved. This confirms that while neighborhoods can enhance skill by sampling more of the forecast, a single deterministic model state in time cannot provide the variability, especially at the kilometer scale, where rapid error growth acts to limit local predictability. Ensembles are able to account for the uncertainty at larger, synoptic scales. The results also show that the rate of decrease in skill with lead time is greater for the deterministic UKV. MOGREPS-UK retains higher skill for longer. The concept of a skill differential is introduced to find the smallest neighborhood size at which the deterministic and ensemble scores are comparable. This was found to be 3 ? 3 (6.6 km) for MOGREPS-UK and 11 ? 11 (16.5 km) for UKV. Comparable scores are between 2% and 40% higher for MOGREPS-UK, depending on the variable. Naively, this would also suggest that an extra 10 km in spatial accuracy is gained by using a kilometer-scale ensemble.