What 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.
We investigated high-resolution simulations of regional climate models (RCMs) driven by ERA-40 reanalyses over areas of selected European countries (Austria, Czech Republic, Hungary, Slovakia and Romania) for the period 1961−1990. RCMs were run at a spatial resolution of 10 km in the framework of the CECILIA project, and their outputs were compared with the E-OBS dataset of gridded observations and RCM simulations at coarser 25 km resolution from the ENSEMBLES project to identify a possible gain from the CECILIA experiments over ENSEM-BLES. Cold biases of air temperature and wet biases of precipitation dominate in the CECILIA simulations. Spatial variability and distribution of the air temperature field are well captured. The precipitation field, relative to observations, often shows inadequately small spatial variability and lowered correlations but is nevertheless comparable to the ENSEMBLES model. Inter-annual variability (IAV) of air temperature is captured differently among seasons but mostly improved in CECILIA compared with ENSEMBLES. Precipitation IAV shows a similar or worse score. The detected weaknesses found within the validation of the CECILIA RCMs are attributed to the resolution dependence of the set of physical parameterizations in the models and the choice of integration domain. The gain obtained by using a high resolution over a small domain (as in CECILIA) relative to a lower resolution (25 km) over a larger domain (as in ENSEMBLES) is clear for air temperature but limited for precipitation.
Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021–2050 and 2071–2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3°C in the first period and 2 and 5°C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of +1 to −1 mm/day. Regional features are amplified by the RCMs, more so in case of the ALADIN family of models.
ABSTRACT:The article describes the attempt to include the intensity-scale technique introduced by Casati et al. (2004) into a set of standardized verifications used in operational centres. The intensity-scale verification approach accounts for the spatial structure of the forecast field and allows the skill to be diagnosed as a function of the scale of the forecast error and intensity of the precipitation events. The intensity-scale method has been used to verify two different resolutions of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational quantitative precipitation forecast (QPF) over France, and to compare the performance of the ECMWF and the Hungarian Meteorological Service operational model (ALADIN) forecasts, run over Hungary. Two case studies have been introduced, which show some interesting insight into the spatial scale of the error. The distribution of daily skill score for an extended period of time is also presented. The intensity-scale technique shows that the forecasts in general exhibit better skill for large-scale events, and lower skill for small-scale and intense events. In the paper, it is mentioned how some of the stringent assumptions on the domain over which the method can be applied, and the availability of the matched forecasts and observations, can limit its usability in an operational environment.
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