Articles | Volume 12, issue 10
Geosci. Model Dev., 12, 4261–4274, 2019
Geosci. Model Dev., 12, 4261–4274, 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.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
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
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.