A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
2019
Geoscientific Model Development
Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from the Weather Research Forecast (WRF) climate model to train deep neural networks (DNNs) and evaluate whether trained DNNs can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using DNNs for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are used in atmospheric
doi:10.5194/gmd-12-4261-2019
fatcat:7envzvg6p5fyxhjvw6bslfb5na