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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

  • Haipeng Lin
  • , Xu Feng
  • , Tzung May Fu
  • , Heng Tian
  • , Yaping Ma
  • , Lijuan Zhang
  • , Daniel J. Jacob
  • , Robert M. Yantosca
  • , Melissa P. Sulprizio
  • , Elizabeth W. Lundgren
  • , Jiawei Zhuang
  • , Qiang Zhang
  • , Xiao Lu
  • , Lin Zhang
  • , Lu Shen
  • , Jianping Guo
  • , Sebastian D. Eastham
  • , Christoph A. Keller
  • Peking University
  • Harvard University
  • Southern University of Science and Technology
  • Tsinghua University
  • Chinese Academy of Meteorological Sciences
  • Massachusetts Institute of Technology
  • Universities Space Research Association

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

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 PM2.5 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 PM2.5, but the WRF-GC model (r=0.68, bias =29%) reproduced the observed daily PM2.5 concentrations over eastern China better than the GEOS-Chem Classic model did (r = 0.72, = 55%). 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 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 http://wrf.geos-chem.org (last access: 10 July 2020).

Original languageEnglish
Pages (from-to)3241-3265
Number of pages25
JournalGeoscientific Model Development
Volume13
Issue number7
DOIs
StatePublished - Jul 16 2020
Externally publishedYes

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