Working group on Orographic Convection


The TEAMx working group on orographic convection, led by Daniel Kirshbaum (McGill University, Canada) and M. Marcello Miglietta (ISAC-CNR, Italy), consists of about 20 scientists from 8 countries and has recently started its activity. Three main research topics have been identified:

  • A pan-Alpine convection initiation climatology, aimed at identifying mechanisms relevant for convective triggering. A good starting point for this activity is to identify the Alpine climatologies already available, determine what methodologies were used, and understand if and how these methods could be extended over a pan-Alpine scale. The expertise in developing algorithms for cell tracking will be combined with radar/satellite/lightning data analysis and with model climatologies based on high-resolution reforecasts over several decades;
  • A convection permitting model inter-comparison, where different models with grid spacing of about 1 km will verified to get further insight into their ability of simulating diurnally-forced and orographic convection. Deterministic model runs (AROME, COSMO, ICON, MOLOCH, WRF, …) will be analyzed together with ensemble forecasting systems on prototypical locally forced Alpine deep-convection events. These are more relevant to TEAMx than synoptically forced events, because they depend more on local PBL exchange processes. A list of relevant meteorological events and/or problematic forecast cases over different regions is being defined (mainly focusing on primary convection triggering);
  • A model inter-comparison of idealized large-eddy simulations (about 100 m grid spacing) of one or more types of orographic convection. Cases of “mechanically” forced convection, with conditionally unstable air forced up and over an Alpine-like ridge, and/or of “thermally” forced convection, with diurnal heating leading to convection initiation over or downwind of the ridge tops, will be considered. These experiments will provide insight into both model predictability and the role of turbulence in shaping the flow, cloud, and precipitation patterns. Two potential strategies are envisioned: one fully idealized using realistic but reduced-complexity input conditions, surface forcings, and terrain; alternatively, a semi-idealized approach that uses real cases as a starting point, from which key atmospheric features are identified and used to progressively simplify the situation until the essential physics of interest are isolated.