Using artificial neural network to better evaluate surface turbulent heat fluxes in weather and climate numerical models

The surface turbulent fluxes, namely sensible and latent heat fluxes, are keys factors governing the boundary layer processes. Therefore, their correct representation in numerical models is crucial for accurate weather forecasts and climate projections. However, the formulation of these fluxes in such models is the second source of uncertainty, leading to incorrect surface-atmosphere interactions in the simulations. Model evaluation is essential to draw development perspectives. Existing methods mostly consist of direct comparison between observed and modelled fluxes, blending other sources of errors such as incoherent grid-scale representation (soil and vegetation types) and inaccurate environmental forcing (radiative fluxes, temperature, moisture and wind speed). Thus, quantifying errors solely due to fluxes formulation is still challenging. This study, within the framework of the French project MOSAI (Model and Observation for Surface-Atmosphere Interactions), aims at proposing a novel evaluation approach to better identify the weakness of numerical models in surface turbulent fluxes formulation. The concept is to freeze the errors due to other sources by using a machine-learning model, notably a multi-layer perceptron, trained to estimate the fluxes from variables describing the conditions in the surface layer. Hourly data collected over several years at three operational instrumented sites of ACTRIS-France research infrastructure (SIRTA in Paris, Météo-pole in Toulouse and P2OA in Lannemezan), are used. Then, after adaptation to the outputs of numerical models involved in the MOSAI project (RegIPSL, LMDZ, AROME and ARPEGE), the trained-perception will be applied to assess their surface turbulent fluxes.