|Title||Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations : An MPAS feasibility study of three extreme weather events in Europe|
|Author(s)||Kramer, Matthijs; Heinzeller, Dominikus; Hartmann, Hugo; Berg, Wim van den; Steeneveld, Gert Jan|
|Source||Climate Dynamics (2018). - ISSN 0930-7575 - p. 1 - 24.|
Meteorology and Air Quality
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Convection-permitting forecast - Föhn - Grey-zone - Hail - MPAS - Numerical weather prediction - Voronoi grid - WRF|
Regional weather forecasting models like the Weather Research and Forecasting (WRF) model allow for nested domains to save computational effort and provide detailed results for mesoscale weather phenomena. The sudden resolution change by nesting may cause artefacts in the model results. On the contrary, the novel global Model for Prediction Across Scales (MPAS) runs on Voronoi meshes that allow for smooth resolution transition towards the desired high resolution in the region of interest. This minimises the resolution-related artefacts, while still saving computational effort. We evaluate the MPAS model over Europe focussing on three mesoscale weather events: a synoptic gale over the North Sea, a föhn effect in Switzerland, and a case of organised convection with hail over the Netherlands. We use four different MPAS meshes (60 km global refined to-3 km (60– 3 km), analogous 30–3 km, 15–3 km, global 3 km) and compare their results to routine observations and a WRF setup with a single domain of 3 km grid spacing. We also discuss the computational requirements for the different MPAS meshes and the operational WRF setup. In general, the MPAS 3 km and WRF model results correspond to the observations. However, a global model at 3 km resolution as a replacement for WRF is not feasible for operational use. More importantly, all variable-resolution meshes employed in this study show comparable skills in short-term forecasting within the high-resolution area at considerably lower computational costs.