Chertova, M. (Maria)

Crommelin, D.T. (Daan)

Jansson, F.R. (Fredrik)

Oord, G. (Gijs) van den

Pelupessy, F.I. (Inti)

Siebesma, A.P. (Pier)

English

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Geophysical Research AbstractsVol. 21, EGU2019-11303, 2019EGU General Assembly 2019© Author(s) 2019. CC Attribution 4.0 license.On the regional superparametrization of OpenIFS by 3D LES modelsFredrik Jansson (2), Gijs van den Oord (1), Inti Pelupessy (1), Maria Chertova (1), Pier Siebesma (3), and DaanCrommelin (2)(1) Netherlands eScience Center, Amsterdam, Netherlands, (2) Centrum Wiskunde & Informatica, Amsterdam, Netherlands,(3) Delft University of Technology, Delft, NetherlandsWe present the superparametrization (SP) of the ECMWF weather forecast model OpenIFS, by three-dimensional,high-resolution, large eddy simulations. Within a selected region, our setup replaces the global model parametriza-tions of boundary layer turbulence, cloud physics and convection processes with tendencies derived from the verti-cal profiles of instances of the Dutch Atmospheric Large Eddy Simulation (DALES). Vice versa, these LES modelsare being forced towards the corresponding local vertical profiles of the global model. We consistently combine thesequential physics scheme of OpenIFS with the Grabowski SP scheme and achieve concurrent execution of the in-dependent DALES models on separate CPU’s. The superparametrized region can be chosen to match the availablecompute resources, and we have implemented mean-state acceleration to speed up the LES time stepping. The cou-pling of the components has been implemented in a Python software layer using the OMUSE multi-scale physicsframework. As a result, our setup yields a cloud-resolving NWP model that displays emergent mesoscale cloudorganization and has the potential to improve the representation of clouds and convection processes in OpenIFS.It allows us to study the interaction of boundary layer physics with the large scale dynamics, to assess cloud andconvection parametrization in the ECMWF model and eventually to improve our understanding of cloud feedbackin climate models.

On the regional superparametrization of OpenIFS by 3D LES models

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