Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 10
Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 10
Development and technical paper
 | 
10 Oct 2019
Development and technical paper |  | 10 Oct 2019

Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi

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Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
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