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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.

Authors :
Lin, Haipeng
Feng, Xu
Fu, Tzung-May
Tian, Heng
Ma, Yaping
Zhang, Lijuan
Jacob, Daniel J.
Yantosca, Robert M.
Sulprizio, Melissa P.
Lundgren, Elizabeth W.
Zhuang, Jiawei
Zhang, Qiang
Lu, Xiao
Zhang, Lin
Shen, Lu
Guo, Jianping
Eastham, Sebastian D.
Keller, Christoph A.
Source :
Geoscientific Model Development. 2020, Vol. 13 Issue 7, p3241-3265. 25p.
Publication Year :
2020

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 PM 2.5 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 PM 2.5 , but the WRF-GC model (r=0.68 , bias =29 %) reproduced the observed daily PM 2.5 concentrations over eastern China better than the GEOS-Chem Classic model did (r=0.72 , bias =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). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1991959X
Volume :
13
Issue :
7
Database :
Academic Search Index
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
144905916
Full Text :
https://doi.org/10.5194/gmd-13-3241-2020