A short review of numerical cloud-resolving models

Abstract

A cloud-resolving model (CRM) allows performing numerical simulations of convective clouds, such as shallow cumulus and stratocumulus, or storms and squall-lines with a resolution on the order of a few tens of metres to a few kilometres over a limited-area 4D (time and space) domain. The development of such models over the past decades is reviewed and their specific features are presented. The latter include a non-hydrostatic dynamic and parameterizations of sub-grid turbulence, microphysical and radiative processes. The capabilities of such models are discussed based on comparisons with observations and model-intercomparison studies. CRMs are used in a variety of ways, from the exploration of cloud phenomenology and process-understanding studies to the development of algorithms for satellite products, as well as to address climate issues and to develop convective and cloud parametrizations for large-scale weather and climate models. A few results illustrating this wide utilization are presented. The continuous increase of computer power induces rapid changes in modelling perspectives and therefore, influences the developments and applications of CRMs. This is discussed together with emerging scientific questions which will further benefit from CRM simulations.

Overview of the Meso-NH model version 5.4 and its applications

“…Initially, LESs were primarily used in constrained idealized configurations (homogeneous initial fields, cyclic lateral boundary conditions). However, now they also concern real case studies with open boundary conditions, sometimes with a downscaling approach using grid-nesting techniques, providing spatio-temporal 25 turbulence characteristics difficult to retrieve from measurements alone (Guichard and Couvreux, 2017).…”

Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain

“…Continuing the theme of parameterizing unresolved motions, we move next to considering schemes used to represent convection over mountainous terrain. Kirshbaum et al [105] gave a recent overview over moist orographic convection and Guichard and Couvreux [106] reviewed cloud-resolving models. Generally, the lifting of air masses in conjunction with mountains enables them to overcome convective inhibition and trigger the generation of new convective cells.…”

Revisiting the Impact of Stochastic Multicloud Model on the MJO Using Low-Resolution ECHAM6.3 Atmosphere Model

“…Moreover, NICAM has the ability to predict MJO events with lead times of up to 1 month with realistic initial conditions (Miura et al 2007;Miyakawa et al 2014). The history and recent developments of CRM are reviewed by Guichard and Couvreux (2017). However, running a global CRM or superparameterized model is a computationally demanding approach, possibly limiting its applicability in global earth system models (ESMs) used for very long integrations.…”