资源环境科技发展态势分析平台

We use the Icosahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection distinguishing between scattered, large scale dynamically forced and frontal convection. We use different ground and satellite based observational data sets, that supply information on ice water content and path, ice cloud cover and cloud top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations.

We find that the timing and geometric extent of the convectively generated cloud shield agrees well with observations while the life time of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates cloud ice water path that lies in-between the different observational data sets but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). The bias in frozen water path and the longevity of the anvil are little affected by modifications of parameters within the microphysical scheme. In particular one of our convective days appeared to be very sensitive to the initial and boundary conditions which had a large impact on the convective triggering, but little impact on the high frozen water path and long anvil life time bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters.

Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding timing, geometrical structure and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.