Convection-Permitting Models: Added Value and Advances in the Representation of Unresolved Physical Processes and Model Uncertainties
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 1026
Special Issue Editor
Special Issue Information
Dear Colleagues,
Convection-Permitting Models (CPMs) are numerical models designed to simulate the atmosphere using horizontal grid spacings sufficient (1 km < Δx < 4 km) to resolve at least part of the dynamics associated with moist convection. It is consequently possible to operate these models without specifically parameterising deep convection, although shallower cumulus clouds still need to be parameterised. Over the years, it has been demonstrated that CPMs, now used operationally by many national weather forecasting centres across the world, clearly improve the predictability of convection-related processes, such as the initiation and occurrence of deep convection, surface rainfall rates, as well as high-impact weather events. More recently, advances in computational capabilities have even permitted the development of global CPMs as well as CPM-based ensemble forecast systems.
In this special issue of Atmosphere, we invite scientific contributions presenting cutting-edge results that demonstrate the added value of CPMs over coarser resolution models. The main focus of this Special Issue is on the representation and predictability of convective-scale processes, including surface precipitation rates or extreme weather events. We also encourage contributions presenting advances that improve the predictive skills of CPMs, in particular concerning the representation of unresolved physical processes and model uncertainties using, for example, stochastic perturbation schemes.
Dr. Julien Savre
Guest Editor
Manuscript Submission Information
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Keywords
- convection-permitting models
- ensemble forecasting
- convective-scale processes
- predictability of convection
- model uncertainties
- physical parameterizations
- stochastic perturbations
