Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
Year: 2020
Type: article
Abstract: . Current turbulence parameterizations in numerical weather prediction models at the mesoscale assume a local equilibrium between production and dissipation of turbulence. As this assumption does not hold at fine horizontal resolutions, improved ways to represent turbulent kinetic energy (TKE) dissipation rate (ε) are needed. Here, we use a 6-week data set of turbulence measurements from 184 sonic anemometers in complex terrain at the Perdigão field campaign to suggest improved representations of dissipation rate. First, we demonstrate that a widely used Mellor, Yamada, Nakanishi, and Niino (MYNN) parameterization of TKE dissipation rate leads to a large inaccuracy and bias in the representation of ε. Next, we assess the potential of machine-learning techniques to predict TKE dissipation rate from a set of atmospheric and terrain-related features. We train and test several machine-learning algorithms using the data at Perdigão, and we find that multivariate polynomial regressions and random forests can eliminate the bias MYNN currently shows in representing ε, while also reducing the average error by up to 30 %. Of all the variables included in the algorithms, TKE is the variable responsible for most of the variability of ε, and a strong positive correlation exists between the two. These results suggest further consideration of machine-learning techniques to enhance parameterizations of turbulence in numerical weather prediction models.
Source: Geoscientific model development
Authors: Nicola Bodini, Julie K. Lundquist, Mike Optis
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,725
Awards: G14908310, G2164033304, G2586609609, G2796167598, G3883580859, G399272915, MRI Collaborative Consortium: Acquisition of a Shared Supercomputer by the Rocky Mountain Advanced Computing Consortium, CAREER: BREEZE: Boundary-layer REsearch and Education ZonE, G4958434179, MRI Collaborative Consortium: Acquisition of a Shared Supercomputer by the Rocky Mountain Advanced Computing Consortium, G5273191617, G5428173658, G645873479, G6545073602, G6671297155, Collaborative Research: Perdigao--The Stable Boundary Layer over Complex Terrain, G7021897321, G8142658735, G847654844, G8929392486, G944719100
OpenAlex ID: W3087330307