Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4271–4285, 2020
Geosci. Model Dev., 13, 4271–4285, 2020
Development and technical paper
16 Sep 2020
Development and technical paper | 16 Sep 2020

Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?

Nicola Bodini et al.

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Cited articles

Albertson, J. D., Parlange, M. B., Kiely, G., and Eichinger, W. E.: The average dissipation rate of turbulent kinetic energy in the neutral and unstable atmospheric surface layer, J. Geophys. Res.-Atmos., 102, 13423–13432, 1997. a
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Berg, L. K., Liu, Y., Yang, B., Qian, Y., Olson, J., Pekour, M., Ma, P.-L., and Hou, Z.: Sensitivity of Turbine-Height Wind Speeds to Parameters in the Planetary Boundary-Layer Parametrization Used in the Weather Research and Forecasting Model: Extension to Wintertime Conditions, Bound.-Lay. Meteorol., 170, 507–518, 2018. a
Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.