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WRF-GC

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About WRF-GC

WRF-GC (Lin et al., 2020; Feng et al., 2021) is a new, online coupled model based on the the regional meteorology model, WRF, and the chemical transport model, GEOS-Chem. The coupling structure is designed to be easy to use, massively parallel, and ready for the future. In particular, either one of the two parent models to be updated independently. This allows WRF-GC to take full advantage of the updates in WRF and GEOS-Chem, respectively, and stay state-of-the-science.

WRF-GC is open-source and free to download and use.

Architectural overview of the WRF-GC model (v2.0). The WRF-GC two-way Coupler (all parts shown in red) includes interfaces to the two parent models, as well as the state conversion and state management modules. The parent models (shown in grey) are standard codes downloaded from their sources, without any modifications. From Feng et al. (2021).


The development of WRF-GC was led by Haipeng LIN, Xu FENG, and Tzung-May FU. In addition, the GEOS-Chem Support Team provided valuable assistance in updating the GEOS-Chem code to accommodate the coupling.


Developers


Version history

Version 3.0

(updated April 24, 2024) WRF-GC v3.0 includes the following major new features and technical updates:

  • Supports GEOS-Chem 14.1.1, KPP 3.0.0, and HEMCO 3.6.2.
  • Supports WRF version 4.4.
  • Infrastructure for specialty simulations. Now can install couplers for fullchem, ch4, and co2 using the install_registry command.
  • Add support for History diagnostics and HEMCO diagnostics ("WRF-GC 3.0 Diagnostics") using pNETCDF.
  • Technical Update: Compiles GEOS-Chem 13+ without using CMake by maintaining the legacy GNU infrastructure.
  • Technical Update: Auto-patching of wrf_io.F90 to support more than 3,000 variables in wrfbdy.
  • Technical Update: No longer inputs initial/boundary conditions for non-advected species through i0{12} switch in registry.chem.
  • Enhancement: Recompile no longer wipes configuration files by automatically calling make install_configs. Configuration files are installed once using make install_registry or others for specialty simulations.
  • Enhancement: New coupler species generation infrastructure shared with CESM2-GC.
  • Enhancement: Support for QV2M met field for online blowing snow emissions.
  • Bugfix: Fix stack corruption issue in chemics_init coordinates.
  • Bugfix: More robust support for get_last_gas due to WRFv4 upstream updates.
  • Bugfix: Month-boundary HEMCO emissions missing timesteps.
  • Bugfix: ParaNOx stability issue via upstream GEOS-Chem 14.0.1 fix. Note that according to Colombi et al., 2023 ACP <https://acp.copernicus.org/articles/23/4031/2023/>_ it may not be necessary to use ParaNOx for high-resolution simulations with WRF-GC.
  • Bugfix: Initial condition input bug for nested domain(s).


Version 2.0

(updated June 24, 2021) WRF-GC v2.0 (Feng et al. (2021)) is an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem chemical model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations.

Major features of WRF-GC v2.0:

  • Updated compatibility with the latest release of GEOS-Chem v12.8.1
  • Aerosol-radiation interactions and aerosol-cloud interactions that can be turned on/off
  • Nested-domain functionality
  • Online lightning NOx emissions
  • Restart run functionality
  • Bug fixes from v1.0


Version 1.0

The first version of WRF-GC (v1.0) (Lin et al., 2020) was released on January 4, 2019. A more mature and stable version (v 1.0) was released in Fall 2019.

(updated Feb 26, 2021) We recommend that all users upgrade to WRF-GC v2.0.

A few notes about v1.0:

  • The coupling between WRF and GEOS-Chem is one-way only, i.e., there is no feedback from GEOS-Chem to WRF.
  • The nested domain capability of WRF is not yet supported.


WRF-GC reference

  1. Lin, H., Feng, X., Fu, T.-M.*, Tian, H., Ma, Y., Zhang, L., Jacob, D.J., Yantosca, R.M., Sulprizio, M.P., Lundgren, E.W., Zhuang, J., Zhang, Q., Lu, X., Zhang, L., Shen, L., Guo, J., Eastham, S.D., Keller, C.A. (2020), WRF-GC (v1.0): online coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.2.1) for regional atmospheric chemistry modeling – Part 1: Description of the one-way model, Geosci. Model Dev., doi:10.5194/gmd-13-3241-2020, Full text
  2. Feng, X., Lin, H., Fu, T.-M.*, Sulprizio, M. P., Zhuang, J., Jacob, D. J., Tian, H., Ma, Y., Zhang, L., Wang, X., Chen, Q., Han, Z. (2021), WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry-meteorology interactions, Geosci. Model. Dev., 14, 3741-3768, doi:10.5194/gmd-14-3741-2021. Full text
  3. Feng, X., Lin, H., and Fu, T.-M.* (2020), WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5165, doi:10.5194/egusphere-egu2020-5165. Link.


Funding for WRF-GC model development

The development of the WRF-GC model is currently supported by the National Natural Science Foundation of China (42325504), the National Key Research and Development Program of China (2023YFC3706205), the Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks (ZDSYS20220606100604008), the Shenzhen Science and Technology Program (KQTD20210811090048025, JCYJ20220818100611024).


Publications using WRF-GC

  1. Kim, H., Park, R. J., Hong, S.-Y., Park, D.-H., Kim, S.-W., Oak, Y. J., Feng, X., Lin, H., Fu, T.-M. (2024), A mixed layer height parameterization in a 3-D chemical transport model: Implications for gas and aerosol simulations. Science of the Total Environment, doi:10.1016/j.scitotenv.2024.176838.
  2. Wu, W., Fu, T.-M.*, Arnold, S.*, Spracklen, D.V., Zhang, A., Tao, W., Wang, X., Hou, Y., Mo, J., Chen, J., Li, Y., Feng, X., Lin, H., Huang, Z., Zheng, J., Shen, H., Zhu, L., Wang, C. Ye, J., Yang, X. (2024), Temperature-dependent evaporative anthropogenic VOC emissions significantly exacerbate regional ozone pollution. Environmental Science & Techonology, doi:10.1021/acs.est.3c09122.Full text
  3. Liu, X., Wang, Y. et al. (2024), Impacts of anthropogenic emissions and meteorology on spring ozone differences in San Antonio, Texas between 2017 and 2021, Science of the Total Environment, 914, 169693, doi:10.1016/j.scitotenv.2023.169693. Full text
  4. Feng, X., Ma, Y., Lin, H., Fu, T.-M.*, Zhang, Y., Wang, X., Zhang, A., Yuan, Y., Han, Z., Mao, J., Wang, D., Zhu, L., Wu, Y., Li, Y., Yang, X. (2023), Impacts of ship emissions on air quality in Southern China: opportunistic insights from the abrupt emission changes in early 2020, Environmental Science & Technology, doi:10.1021/acs.est.3c04155. PDF Full text.
  5. Liu, X., Wang, Y. et al. (2023), Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas. Geoscientific Model Development, 16, 5493-5514, doi:10.5194/gmd-16-5493-2023. Full text
  6. 刘婵芳, 张傲星*, 房庆, 叶毓婧, 杨红龙, 陈炯恺, 吴雯潞, 侯岳, 莫佳佳, 傅宗玫. (2023), 深圳市2022年春季新冠疫情管控期间空气质量分析[J/OL], 环境科学:1-18, doi:10.13227/j.hjkx.202205313. 已接收
  7. Zhang, A., Fu, T.-M.*, Feng, X., Guo, J., Liu, C., Chen, J., Mo, J., Zhang, X., Wang, X., Wu, W., Hou, Y., Yang, H., Lu, C. (2023), Deep learning-based ensemble forecasts and predictability assessments for surface ozone pollution. Geophysical Research Letters, e2022GL102611, doi:10.1029/2022GL102611. Full text
  8. Xu, X., Feng, X., Lin, H., Zhang, P., Huang, S., Song, Z., Peng, Y., Fu, T.-M., Zhang, Y. (2022), Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0, Geoscientific Model Development, 15, 3845–3859, doi:10.5194/gmd-15-3845-2022.Full text.
  9. Hu, S., Wang, D., Wu, J., Zhou, L., Feng, X., Fu, T.-M., Yang, X., Ziegler, A. D., Zeng, Z. (2021), Aerosol presence reduces the diurnal temperature range: an interval when the COVID-19 pandemic reduced aerosols revealing the effect on climate, Environmental Science: Atmospheres, doi:10.1039/D1EA00021G. Full text
  10. Zhang, R., Zhang, Y., Lin, H., Feng, X., Fu, T.-M., Wang, Y. (2020), NOx emission reduction and recovery during COVID-19 in East China, Atmosphere, 11(4), 433, doi:10.3390/atmos11040433. Full text

Using WRF-GC

Obtaining the code

WRF-GC can be downloaded from GitHub. We also encourage you to WRF-GC google group (WRF-GC@googlegroups.com) to stay updated with the model status.


Getting started

WRF-GC User Guide This is the updated version of WRF-GC v3.0 User Guide.


Model description paper of WRF-GC (v2.0). Published in Geoscientific Model Development

Starter kit for use in the WRF-GC model clinic This is a package of an example simulation using WRF-GC to be used with the tutorial above.

Model description paper of WRF-GC (v1.0). Published in Geoscientific Model Development

Overview of the WRF-GC as of May 2019


Help with WRF-GC

WRF-GC google group

The WRF-GC google group (WRF-GC@googlegroups.com) is a place for WRF-GC users to ask questions and exchange experiences about using the WRF-GC model.

To join the google group, please sign in your google account and search for WRF-GC on [groups.google.com Google Groups] and click 'Ask to join'. Your group membership should be processed by the group managers shortly.


Contact us

If you encounter any problems running WRF-GC or have suggestions for model improvements, please email us (fuzm AT sustech DOT edu DOT cn).

  • This page was last modified on 12 October 2024, at 15:26.
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