Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
Abstract
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 models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics and turbulence, as well as velocity, temperature, and humidity profiles within the boundary layer are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical climate model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware DNN, which takes account of underlying domain structure (e.g., nonlocal mixing), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. Results also show that a single trained DNN from one location can produce predictionsmore »
- Authors:
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
- OSTI Identifier:
- 1570018
- Alternate Identifier(s):
- OSTI ID: 1607653
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Journal Article: Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 12 Journal Issue: 10; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Wang, Jiali, Balaprakash, Prasanna, and Kotamarthi, Rao. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Germany: N. p., 2019.
Web. doi:10.5194/gmd-12-4261-2019.
Wang, Jiali, Balaprakash, Prasanna, & Kotamarthi, Rao. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Germany. https://doi.org/10.5194/gmd-12-4261-2019
Wang, Jiali, Balaprakash, Prasanna, and Kotamarthi, Rao. 2019.
"Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model". Germany. https://doi.org/10.5194/gmd-12-4261-2019.
@article{osti_1570018,
title = {Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model},
author = {Wang, Jiali and Balaprakash, Prasanna and Kotamarthi, Rao},
abstractNote = {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 models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics and turbulence, as well as velocity, temperature, and humidity profiles within the boundary layer are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical climate model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware DNN, which takes account of underlying domain structure (e.g., nonlocal mixing), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. Results also show that a single trained DNN from one location can produce predictions of wind speed, temperature, and water vapor profiles over nearby locations with similar terrain conditions with correlations higher than 0.9 when compared with the WRF simulations used as the training dataset.},
doi = {10.5194/gmd-12-4261-2019},
url = {https://www.osti.gov/biblio/1570018},
journal = {Geoscientific Model Development (Online)},
issn = {1991-9603},
number = 10,
volume = 12,
place = {Germany},
year = {Thu Oct 10 00:00:00 EDT 2019},
month = {Thu Oct 10 00:00:00 EDT 2019}
}
Web of Science
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