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
<p>We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models....
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Copernicus Publications
2020-07-01
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Series: | Geoscientific Model Development |
Online Access: | https://gmd.copernicus.org/articles/13/3241/2020/gmd-13-3241-2020.pdf |
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English |
format |
Article |
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DOAJ |
author |
H. Lin H. Lin X. Feng T.-M. Fu T.-M. Fu H. Tian Y. Ma L. Zhang D. J. Jacob R. M. Yantosca M. P. Sulprizio E. W. Lundgren J. Zhuang Q. Zhang X. Lu X. Lu L. Zhang L. Shen J. Guo S. D. Eastham C. A. Keller |
spellingShingle |
H. Lin H. Lin X. Feng T.-M. Fu T.-M. Fu H. Tian Y. Ma L. Zhang D. J. Jacob R. M. Yantosca M. P. Sulprizio E. W. Lundgren J. Zhuang Q. Zhang X. Lu X. Lu L. Zhang L. Shen J. Guo S. D. Eastham C. A. Keller 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 Geoscientific Model Development |
author_facet |
H. Lin H. Lin X. Feng T.-M. Fu T.-M. Fu H. Tian Y. Ma L. Zhang D. J. Jacob R. M. Yantosca M. P. Sulprizio E. W. Lundgren J. Zhuang Q. Zhang X. Lu X. Lu L. Zhang L. Shen J. Guo S. D. Eastham C. A. Keller |
author_sort |
H. Lin |
title |
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 |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
publisher |
Copernicus Publications |
series |
Geoscientific Model Development |
issn |
1991-959X 1991-9603 |
publishDate |
2020-07-01 |
description |
<p>We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models.
WRF-GC offers regional modellers access to the latest GEOS-Chem chemical module, which is state of the science, well documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC uses unmodified copies of WRF and GEOS-Chem from their respective sources; the coupling structure allows future versions of either one of the two parent models to be integrated into WRF-GC with relative ease. Within WRF-GC, the physical and chemical state variables are managed in distributed memory and translated between WRF and GEOS-Chem by the WRF-GC coupler at runtime. We used the WRF-GC model to simulate surface PM<span class="inline-formula"><sub>2.5</sub></span> concentrations over China during 22 to 27 January 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem Classic nested-China simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM<span class="inline-formula"><sub>2.5</sub></span>, but the WRF-GC model (<span class="inline-formula"><i>r</i>=0.68</span>, bias <span class="inline-formula">=29</span> %) reproduced the observed daily PM<span class="inline-formula"><sub>2.5</sub></span> concentrations over eastern China better than the GEOS-Chem Classic model did (<span class="inline-formula"><i>r</i>=0.72</span>, bias <span class="inline-formula">=55</span> %). This was because the WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the pollution meteorology during the study period.
The WRF-GC model is parallelized across computational cores and scales well on massively parallel architectures. In our tests where the two<span id="page3242"/> models were similarly configured, the WRF-GC simulation was 3 times more efficient than the GEOS-Chem Classic nested-grid simulation due to the efficient transport algorithm and the Message Passing Interface (MPI)-based parallelization provided by the WRF software framework. WRF-GC v1.0 supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no chemical feedbacks. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under way. The WRF-GC model is open source and freely available from <span class="uri">http://wrf.geos-chem.org</span> (last access: 10 July 2020).</p> |
url |
https://gmd.copernicus.org/articles/13/3241/2020/gmd-13-3241-2020.pdf |
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doaj-0665464609314577849d6bcd7b4ef50f2020-11-25T03:43:26ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032020-07-01133241326510.5194/gmd-13-3241-2020WRF-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 modelH. Lin0H. Lin1X. Feng2T.-M. Fu3T.-M. Fu4H. Tian5Y. Ma6L. Zhang7D. J. Jacob8R. M. Yantosca9M. P. Sulprizio10E. W. Lundgren11J. Zhuang12Q. Zhang13X. Lu14X. Lu15L. Zhang16L. Shen17J. Guo18S. D. Eastham19C. A. Keller20Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USADepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaState Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, ChinaShenzhen Institute of Sustainable Development, Southern University of Science and Technology, Shenzhen, Guangdong, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, ChinaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USADepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, ChinaHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USAState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, ChinaLaboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USAUniversities Space Research Association, Columbia, MD, USA<p>We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models. WRF-GC offers regional modellers access to the latest GEOS-Chem chemical module, which is state of the science, well documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC uses unmodified copies of WRF and GEOS-Chem from their respective sources; the coupling structure allows future versions of either one of the two parent models to be integrated into WRF-GC with relative ease. Within WRF-GC, the physical and chemical state variables are managed in distributed memory and translated between WRF and GEOS-Chem by the WRF-GC coupler at runtime. We used the WRF-GC model to simulate surface PM<span class="inline-formula"><sub>2.5</sub></span> concentrations over China during 22 to 27 January 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem Classic nested-China simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM<span class="inline-formula"><sub>2.5</sub></span>, but the WRF-GC model (<span class="inline-formula"><i>r</i>=0.68</span>, bias <span class="inline-formula">=29</span> %) reproduced the observed daily PM<span class="inline-formula"><sub>2.5</sub></span> concentrations over eastern China better than the GEOS-Chem Classic model did (<span class="inline-formula"><i>r</i>=0.72</span>, bias <span class="inline-formula">=55</span> %). This was because the WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the pollution meteorology during the study period. The WRF-GC model is parallelized across computational cores and scales well on massively parallel architectures. In our tests where the two<span id="page3242"/> models were similarly configured, the WRF-GC simulation was 3 times more efficient than the GEOS-Chem Classic nested-grid simulation due to the efficient transport algorithm and the Message Passing Interface (MPI)-based parallelization provided by the WRF software framework. WRF-GC v1.0 supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no chemical feedbacks. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under way. The WRF-GC model is open source and freely available from <span class="uri">http://wrf.geos-chem.org</span> (last access: 10 July 2020).</p>https://gmd.copernicus.org/articles/13/3241/2020/gmd-13-3241-2020.pdf |