Preprints
https://doi.org/10.5194/egusphere-2025-2504
https://doi.org/10.5194/egusphere-2025-2504
10 Jun 2025
 | 10 Jun 2025

An update of shallow cloud parameterization in the AROME NWP model

Adrien Marcel, Sébastien Riette, Didier Ricard, and Christine Lac

Abstract. The representation of shallow clouds in numerical weather prediction models remains a challenge for the parameterizations of Atmospheric Boundary Layer (ABL). Previous evaluations of the AROME model have shown radiative budget weaknesses, which were later attributed to a lack of shallow clouds, especially stratocumulus and small cumulus. In this study, we investigate the difficulties of the AROME model to represent the ABL and the associated low clouds, and we provide consistent updates of the Eddy Diffusivity Mass Flux (EDMF) scheme (shallow convection plus turbulence schemes), the subgrid cloud scheme and the associated precipitation. For this purpose, we use the well-known Single Column Model (SCM) versus Large Eddy Simulation (LES) comparison approach to evaluate the modifications. Additionally, the semi-automatic HighTune Explorer tool helps us to explore the free parameter space associated with the modified parameterizations. The modifications are evaluated using several documented cases of boundary layer development, from stratocumulus to precipitating cumulus clouds, including a transition case. Although physical inconsistencies and questionable assumptions still remain in AROME and need to be clarified, the modifications improve the consistency between parameterizations and yield simulations in better agreement with LES, in particular for the diurnal cycle of clouds and the representation of non-local turbulence.

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Adrien Marcel, Sébastien Riette, Didier Ricard, and Christine Lac

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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-2504', Anonymous Referee #1, 09 Jul 2025
  • RC2: 'Comment on egusphere-2025-2504', Anonymous Referee #2, 13 Jul 2025
Adrien Marcel, Sébastien Riette, Didier Ricard, and Christine Lac
Adrien Marcel, Sébastien Riette, Didier Ricard, and Christine Lac

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Short summary
This paper provides substantial consistent updates to the atmospheric boundary layer schemes of the AROME model, yet they can be used for both forecasting and climate modelling. The study employs a single-column model versus large eddy simulations comparison and uses a machine learning tool to calibrate parameterizations. The model's ability to simulate shallow clouds has been enhanced, especially for shallow precipitating cumulus and stratocumulus clouds.
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