Evaluation of turbulence parametrizations in convective clouds and their environment based on a large‐eddy simulation

Abstract

The representation of deep convective clouds by convection‐permitting models could be improved by parametrizing the subgrid turbulent fluxes better. Following the work of Verrelle et al., a large‐eddy simulation (LES) of a population of convective clouds at 50‐m grid spacing was explored during the cloud life cycle to characterize the second‐order moment turbulent fluxes. The reference turbulence fields were deduced by coarse‐graining the LES outputs at horizontal grid resolutions of 500 m and 1 and 2 km. The ability of three parametrizations—the traditional K‐gradient model of Cuxart et al. (CBR), the Smagorinsky formulation, and the Moeng approach (MOENG) based on the horizontal gradients of the resolved variables—to reproduce the thermodynamical and dynamical fluxes was assessed in an offline configuration via a comparison with the reference fields. MOENG was the most appropriate scheme to represent the vertical and horizontal heat fluxes at all grid spacings in the clouds and their environment over the entire cloud life cycle, including the appropriate representation of countergradient areas. It also represented the vertical and horizontal moisture fluxes at 1‐km and 500‐m grid spacings best, while its advantage was reduced at a grid spacing of 2 km, as CBR performed slightly better for the statistical scores. MOENG was also able to represent the dynamical covariances well at grid spacings of 1 km and 500 m, even though the statistical scores were not as good as those obtained for the thermodynamical fluxes. The dynamical variances were well represented by CBR; however, for this offline evaluation, the subgrid turbulent kinetic energy, present in CBR formulations, is computed directly from the LES outputs, giving CBR an advantage over the two other diagnosed parametrizations. Also, the reference fluxes revealed an anisotropic deformation of turbulence throughout the troposphere, which was only captured by MOENG.

Hectometric-scale simulations of a Mediterranean heavy-precipitation event during the Hydrological cycle in the Mediterranean Experiment (HyMeX) first Special Observation Period (SOP1)

“…The eddies near the cloud edges, which are subgrid at 450 m horizontal resolution, are not represented by the turbulence scheme. These results are in a good agreement with those obtained by Verrelle et al (2017) and Strauss et al (2019), who have shown that a commonly used eddy-diffusivity turbulence scheme underestimates the TKE at kilometric and hectometric (500 m) horizontal resolution, especially at the cloud edges but also in the updraught cores where the thermal production is misrepresented as the scheme does not enable the countergradient structures present in the updraught to be reproduced.…”

Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations

“…The decision of taking a threshold of 1m s −1 for updraft and downdraft is motivated by the fact that we have to take into account the measurement uncertainty (less than 0.25-0.5m s −1 ). Additionally, we know that the variance of vertical turbulence is about 1.5 m 2 s −2 (taken from large eddy simulations at 50 m resolution; Verrelle et al, 2017;Strauss et al, 2019). The fact that median w ret for the merged dataset in MCS reflectivity zones 2 to 6 is smaller than 1 m s −1 confirms our decision to use a threshold of 1 m s −1 .…”

“…Between the four MCS locations, differences of aerosol loads and available ice nuclei might exist. Despite these possible differences, ice crystal formation mechanisms may be primarily controlled by dynamics, thermodynamics, and (particularly) secondary ice production rather than the pri- mary nucleation (Field et al, 2016;Phillips et al, 2018;Yano and Phillips, 2011) that regulates the concentrations of hydrometeors beyond ∼ 55 µm, making these concentrations rather similar for different MCS locations.…”

A Path‐Tracing Monte Carlo Library for 3‐D Radiative Transfer in Highly Resolved Cloudy Atmospheres

“…At each interaction with the medium, the contribution of the direct Sun, transmitted along the tracked path, is added to the path weight, as per the local estimate method in a backward version. (b) Image of a high‐resolution congestus cloud (Strauss et al, ) over a complex ground rendered with 4,096 paths computed for each of the three spectral components of each of the 1,280 × 720 pixels (11,324,620,800 paths in total). The camera and Sun setup is described in Table in Appendix .…”

“…Such rendering algorithms are also useful for evaluating the inversion procedures used to retrieve cloud parameters from satellite images. (Strauss et al, 2019) over a complex ground rendered with 4,096 paths computed for each of the three spectral components of each of the 1,280 × 720 pixels (11,324,620,800 paths in total). The camera and Sun setup is described in Table B1 in Appendix B.…”