The tuning tool High Tune Explorer

Two papers describing the tuning tool, High Tune Explorer, have recently been submitted to Journal of Advances in Modelling Earth Systems

Article published on 22 July 2020
last modification on 27 August 2021

A fruitful collaboration between University of Exeter, LMD and CNRM lead to the development of a tuning tool, High-Tune Explorer. The description of this tool, the philosophy that motivated its development and several examples of applications have been gathered in two research papers that have been recently submitted at Journal of Advances in Modelling Earth Systems. They are joined below:
Process-based climate model development harnessing machine learning: I A new tool for parameterization improvement, Couvreux F, F Hourdin, D Williamson, R Roehrig, V Volodina, N Villefranque, C Rio, O Audouin, J Salter, E Bazile, F Brient, F Favot, R Honnert, M-P Lefebvre, J-B Madeleine, Q Rodier, W Xu, submitted to JAMES (June 2020)

, aussi disponible sous :


Process-based climate model development harnessing machine learning: II: model calibration from single column to global, Hourdin F, D Williamson, C Rio, F Couvreux, R Roehrig, N Villefranque, I Musat, L Fairhead, F B Diallo, V Volodina, submitted to JAMES (June 2020)

Two other papers follow those first two:

The paper Process-based climate model development harnessing machine learning: III. The Representation of Cumulus Geometry and their 3D Radiative Effects - Villefranque et al 2021 in JAMES. It focuses on the evaluation and calibration of the parameters implied in the geometric representation of clouds in the radiative transfer scheme, ecRad, using radiative references obtained with Monte-Carlo 3D radiative transfer in LES 3D cumulus fields.

The high-tune explorer tool has been applied to the calibration of the turbulence parameterization of the ARPEGE-Climat climate model for stable boundary layer in the framework of the PhD thesis of O Audouin (supervised by Romain Roehrig and Fleur Couvreux) and those results have been published in JAMES:
Modeling the GABLS4 strongly stable boundary layer with a GCM parameterization: parametric sensitivity or intrinsic limits? O Audouin, R Roehrig, F Couvreux, D Williamson, JAMES