edu.ucar.opensky::articles:25611 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T20:06:01.848225 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d73j3hrs https://doi.org/10.5281/zenodo.6478730 https Related Dataset #1 On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models https://doi.org/10.5281/zenodo.6884011 https Related Dataset #2 On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model 2022-08-27T00:00:00Z publication https://n2t.org/ark:/85065/d73j3hrs https://doi.org/10.1029/2021JD036214 Muñoz-Esparza, Domingo author Becker, Charles author Sauer, Jeremy author Gagne, David John author Schreck, John S. author Kosović, Branko author UCAR/NCAR - Library publisher Recently, machine learning techniques have been employed to develop improved models for predicting surface-layer fluxes of momentum, heat and moisture based on field observations. Herein we explore refinement to these models, in particular artificial neural networks (NN), and investigate their applicability within an atmospheric large-eddy simulation model as an alternative to the widely adopted standard of Monin - Obukhov (MO) similarity theory. Atmospheric boundary layer (ABL) simulations under different stability conditions are carried out for a variety of scenarios of increasing complexity, from dry steady neutral boundary layers to moist diurnal cycle. Simulations using the NN models result in predicted flux differences with respect to the corresponding MO simulations that are consistent with NN model skill in predicting the tower observations. These differences lead to notable modifications of mean and turbulence quantities throughout the ABL. While these NN models provide an alternative to improve upon MO, it is demonstrated that thorough scrutiny in design and evaluation is necessary to prevent unphysical predictions and establish a robust and generalizable parameterization. Design aspects considered include input feature engineering, applicability under inputs obtained from different heights, biased predictions due to climatological fingerprints, and sensitivity to the choice of activation function. In this context, it is shown how an atmospheric model can contribute toward efficiently investigating these relevant aspects, including expanding the training data set to generalize the NN model to a range of surface roughness values. Finally, we outline remaining challenges to be addressed toward developing a universal parameterization for surface-layer fluxes using machine learning techniques. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2022 American Geophysical Union. None eng geoscientificInformation