The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

Abstract

Abstract. Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which 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 agree well with observations, while the lifetime 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 a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. 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 lifetime 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 the 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.

Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation

“…The application of a sat-uration adjustment technique is considered to be appropriate because almost all clouds (except extremely maritime ones) relax rapidly to the thermodynamic equilibrium between water vapor and water drops (Seifert and Beheng, 2006a). Other recent studies on aerosol-cloud interactions with the ICON model also make use of the saturation adjustment technique (e.g., Seifert et al, 2012;Rieger et al, 2015;Heinze et al, 2017;Costa-Surós et al, 2020;Rybka et al, 2021). Heterogeneous ice nucleation in the immersion and deposition nucleation modes is calculated based on mineral dust concentrations described in Hande et al (2015), whereas homogeneous ice nucleation is treated following Kärcher and Lohmann (2002) and Kärcher et al (2006).…”

Accurate Absolute Measurements of Liquid Water Content (LWC) and Ice Water Content (IWC) of Clouds and Precipitation with Spectrometric Water Raman Lidar

“…To overcome these limitations, efforts are made to develop a retrieval technique which allows one to obtain estimates of IWC under all measurement conditions. The work is ongoing, but initial results are encouraging as first applications demonstrate (Strandgren 2018;Rybka et al 2021).…”

Sensitivity of cloud phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals

“…RTTOV is a fast radiative transfer model for simulating top-of-atmosphere radiances from passive visible, infrared, and microwave downward-viewing satellite radiometers. It has been widely used in simulating synthetic satellite images and assimilating radiances in numerical models (Saunders et al, 2018;Pscheidt et al, 2019;Senf et al, 2020;Geiss et al, 2021;Rybka et al, 2021). channels (at 3.9, 6.2, 7.3, 8.7, 9.7, 10.8, 12.0, and 13.4 µm) and reflectance from 3 channels (at 0.6, 0.8, and 1.6 µm) simulated by the RTTOV model are used as input to run the remote sensing retrieval algorithms to derive CLAAS-2-like and SEVIRI_ML-like retrievals, named ICON_RTTOV_CLAAS-2 and ICON_RTTOV_SEVIRI_ML products, respectively.…”