Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Discussion papers
https://doi.org/10.5194/gmd-2019-79
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-79
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 29 Apr 2019

Development and technical paper | 29 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

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

Jiali Wang1, Prasanna Balaprakash2, and Rao Kotamarthi1 Jiali Wang et al.
  • 1Environmental Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA
  • 2Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA

Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from a set of simulations performed using the Weather Research Forecast (WRF) model to train deep neural networks and evaluate whether trained models can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using deep neural networks for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are commonly used in atmospheric models to represent the diurnal variation of the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics of the atmospheric boundary layer, mixing and turbulence within the boundary layer, velocity, temperature, and humidity profiles 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 numerical weather model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware deep neural network, which takes account of underlying domain structure that are locality specific (e.g., terrain, spatial dependence vertically), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. We then assess the spatial transferability of the domain-aware neural networks by using a trained model from one location to nearby locations. Results show that a single trained model from a location over the midwestern United States produces predictions of wind components, temperature, and water vapor profiles over the entire diurnal cycle and all nearby locations with errors less than a few percent when compared with the WRF simulations used as the training dataset.

Jiali Wang et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
  • RC1: 'Review', Anonymous Referee #1, 05 Jun 2019 Printer-friendly Version
  • RC2: 'Review', Anonymous Referee #2, 06 Jun 2019 Printer-friendly Version
Jiali Wang et al.
Jiali Wang et al.
Viewed  
Total article views: 265 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
193 71 1 265 3 5
  • HTML: 193
  • PDF: 71
  • XML: 1
  • Total: 265
  • BibTeX: 3
  • EndNote: 5
Views and downloads (calculated since 29 Apr 2019)
Cumulative views and downloads (calculated since 29 Apr 2019)
Viewed (geographical distribution)  
Total article views: 229 (including HTML, PDF, and XML) Thereof 228 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 20 Jul 2019
Publications Copernicus
Download
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.
Parameterizations are frequently used in models representing physical phenomena and are often...
Citation