Publication Date:
2016-07-13
Description:
Three storm-scale ensemble prediction models (AROME-EPS, COSMO-DE-EPS, and MOGREPS-UK) were combined over Western Europe to create two convection-allowing, multi-model ensemble prediction systems (EPSs) with the goal of improving ensemble spread-skill relationships and probabilistic forecasting ability. A shared, uniform grid of _2.2 km resolution was used, containing two regions where model domains overlapped (AROME-EPS/COSMODE-EPS and AROME-EPS/MOGREPS-UK).Verification was conducted over a fivemonth period spanning two years using near-surface observations of wind, temperature, relative humidity and precipitation. Bias correction was also applied to each model in order to remove systematic error and to better assess the added value of the multi-model ensembles. Analyses of EPS errors and ensemble scores are presented, including comparisons between individual EPS and multi-model scores. Verification results show that the multi-model ensembles exhibit generally lower RMSE, increased spread, and improved ROC, ROCA, and Brier scores than the individual EPSs. In addition, a case study was selected to highlight discrepancies in precipitation frequency bias between the AROME-EPS and COSMODE-EPS models, attributable to differences in distribution and intensity of precipitation. When combined as a multi-model EPS, the sixhour precipitation accumulation forecast for this case study matched the observations better than the individual EPS forecasts. Together, these results highlight the potential advantage of using multiple models with differing dynamics and physics parameterizations when developing a convection-allowing EPS.
Print ISSN:
0035-9009
Electronic ISSN:
1477-870X
Topics:
Geography
,
Physics
