Articles | Volume 12, issue 10
Geosci. Model Dev., 12, 4261–4274, 2019
https://doi.org/10.5194/gmd-12-4261-2019
Geosci. Model Dev., 12, 4261–4274, 2019
https://doi.org/10.5194/gmd-12-4261-2019

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 et al.

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AR by V. Rao Kotamarthi on behalf of the Authors (10 Aug 2019)  Author's response    Manuscript
ED: Publish as is (01 Sep 2019) by Richard Neale
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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.