Is an Artificial Neural Network Able to Reproduce Atmospheric Turbulent Fluxes of a Large Eddy Simulation?

This study explores the potential of Artificial Neural Networks (ANNs) for the calculation of momentum and sensible heat fluxes. The ANN is applied on idealized Large Eddy Simulation (LES) data. The LES test cases used to train the ANN correspond to convective conditions with partially low wind speeds and heterogeneous surfaces. To enable the ANN to learn the systematics of such conditions, the input variables for the ANN include the variables that are used in Monin Obukhov Similarity Theory (MOST) and an additional variable that accounts for the heterogeneity of the land surface. Simulation data is averaged over 30 min and different spatial scales. Our findings show that the modeling skill is generally higher for momentum flux than for sensible heat flux. Also, the performance increases with larger spatial-averaging scales. The ANN calculates momentum flux with a correlation of 0.81 and a normalized RMSE of 0.59 for a single grid point. On a spatial-averaging scale of 4000 m, the correlation changes to almost 1.00 and the normalized RMSE to 0.05. The importance of each input variable for model performance is determined with a feature importance weighting. Their relative importance depends strongly on the spatial-averaging scale. The importance of the variable that represents the influence of surface heterogeneity is low at smaller spatial-averaging scales, but increases at larger averaging scales. However, its contribution to the modeling skill is small. Reducing the number of input variables to two results in a substantial loss of performance. Although our results demonstrate the potential of this approach to improve the calculation of momentum and sensible heat fluxes, it is also clear that there are simplifications and limitations in the present setup that need to be overcome to assess general applicability. These include the height of analysis (40 m instead of 10 m or less), the exclusion of all latent heat processes, the exclusion of stable conditions, the data coverage of the required parameter space, and the usage of only surface roughness length to define surface heterogeneity.