A New Lagrangian-Eulerian Coupling Model System

Rudklao Manonom; Lei Xiaoen; Prungchan Wongwises

doi:10.1007/s00376-000-0022-9

Impact Factor: 5.8

Volume 17 Issue 4

Oct. 2000

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2000 > 17(4): 587-600

A New Lagrangian-Eulerian Coupling Model System

1.
Business Management Department; Electricity Generating Authority of Thailand; 53 Charan Sanit Wong Road. Bang Krui. Nonthaburi 11130. Thailand,LAPC; Institute of Atmospheric Physics（IAP）,Chinese Academy of Sciences; Beijing,King Mongkut`s University of Technology Thouburi, 91 Pracha-Utit Road, Bangmod, Thongkru, Bangkok 10140,Thailand

doi: 10.1007/s00376-000-0022-9

Abstract
References
Related papers
PDF

Abstract:
A new Lagrangiann-Eulerian coupling model system is developed to study regional air quality. The system consists of mesoscale dynamical meteorological model (MM), Monte-Carlo model (MCM), parameterized model on planetary boundary layer (PBL) turbulent statistics, dry and wet removal model, and Eulerian nonlinear chemical model (ENCM). The physical, chemical and biological processes on air pollutants are considered comprehensively. 3-D distribution laws for acidic gaseous pollutants (SO2 and NOx) emitted by Thai Mae Moh Power Plant and the secondary pollutants are studied in detail. The results simulated by the coupling model system are in good agreement with observational concentration data.
- MM,
- MCM,
- ENCM,
- Coupling model system
References

[1]	Hailiang ZHANG, Yongfu XU, Long JIA, Min XU, 2021: Smog Chamber Study on the Ozone Formation Potential of Acetaldehyde, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1238-1251. doi: 10.1007/s00376-021-0407-5
[2]	XU Zhifang, GE Wenzhong, DANG Renqing, Toshio IGUCHI, Takao TAKADA, 2003: Application of TRMM/PR Data for Numerical Simulations with Mesoscale Model MM5, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 185-193. doi: 10.1007/s00376-003-0003-x
[3]	MA Yan, CHEN Shang, 2007: Validation of the Polar MM5 for Use in the Simulation of the Arctic River Basins, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 863-874. doi: 10.1007/s00376-007-0863-6
[4]	WANG Jun, BAO Qing, Ning ZENG, LIU Yimin, WU Guoxiong, JI Duoying, 2013: Earth System Model FGOALS-s2: Coupling a Dynamic Global Vegetation and Terrestrial Carbon Model with the Physical Climate System Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1549-1559. doi: 10.1007/s00376-013-2169-1
[5]	Ruichao LI, Jinbo XIE, Zhenghui XIE, Binghao JIA, Junqiang GAO, Peihua QIN, Longhuan WANG, Si CHEN, 2023: Coupling of the Calculated Freezing and Thawing Front Parameterization in the Earth System Model CAS-ESM, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1671-1688. doi: 10.1007/s00376-023-2203-x
[6]	Xiao Qingnong, Guo Weidong, Zhou Xiaoping, 1996: Preliminary Results from Numerical Experiments of a Heavy Rain Process with PENN STATE / NCAR MM5, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 539-547. doi: 10.1007/BF03342044
[7]	SHI Xiaoying, MAO Jiafu, WANG Yingping, DAI Yongjiu, TANG Xuli, 2011: Coupling a Terrestrial Biogeochemical Model to the Common Land Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1129-1142. doi: 10.1007/s00376-010-0131-z
[8]	MIAO Yucong, LIU Shuhua, CHEN Bicheng, ZHANG Bihui, WANG Shu, LI Shuyan, 2013: Simulating Urban Flow and Dispersion in Beijing by Coupling a CFD Model with the WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1663-1678. doi: 10.1007/s00376-013-2234-9
[9]	Ji jinjun, 1989: Atmosphere-Ocean Coupling Schemes in a One-Dimensional Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 6, 275-288. doi: 10.1007/BF02661534
[10]	Liu Hui, Jin Xiangze, Zhang Xuehong, Wu Guoxiong, 1996: A Coupling Experiment of an Atmosphere and an Ocean Model with a Monthly Anomaly Exchange Schem, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 133-146. doi: 10.1007/BF02656857
[11]	Jing ZOU, Zhenghui XIE, Chesheng ZHAN, Feng CHEN, Peihua QIN, Tong HU, Jinbo XIE, 2019: Coupling of a Regional Climate Model with a Crop Development Model and Evaluation of the Coupled Model across China, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 527-540. doi: 10.1007/s00376-018-8160-0
[12]	ZhouTianjun, Zhang Xuehong, Yu Yongqiang, Yu Rucong, Liu Xiying, Jin Xiangze, 2000: Response of IAP/ LASG GOALS Model to the Coupling of Air-Sea Fresh Water Exchange, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 473-486. doi: 10.1007/s00376-000-0037-2
[13]	Zeng Xinmin, Zhao Ming, Su Bingkai, 2000: A Numerical Study on Effects of Land-Surface Heterogeneity from ‘Combined Approach’ on Atmospheric Process Part II: Coupling-Model Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 241-255. doi: 10.1007/s00376-000-0007-8
[14]	Zhizhen XU, Jing CHEN, Mu MU, Guokun DAI, Yanan MA, 2022: A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1432-1450. doi: 10.1007/s00376-022-1341-x
[15]	REN Hongli, CHOU Jifan, HUANG Jianping, ZHANG Peiqun, 2009: Theoretical Basis and Application of an Analogue- Dynamical Model in the Lorenz System, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 67-77. doi: 10.1007/s00376-009-0067-3
[16]	YANG Yang, REN Rongcai, Ming CAI, RAO Jian, 2015: Attributing Analysis on the Model Bias in Surface Temperature in the Climate System Model FGOALS-s2 through a Process-Based Decomposition Method, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 457-469. doi: 10.1007/s00376-014-4061-z
[17]	Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2016: Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System: Sequential Implementation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 659-672. doi: 10.1007/ s00376-015-5155-y
[18]	REN Rongcai, YANG Yang, 2012: Changes in Winter Stratospheric Circulation in CMIP5 Scenarios Simulated by the Climate System Model FGOALS-s2, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1374-1389. doi: 10.1007/s00376-012-1184-y
[19]	XIAO Chuliang and ZHANG Yaocun, , 2013: Simulation of the Westerly Jet Axis in Boreal Winter by the Climate System Model FGOALS-g2, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 754-765. doi: 10.1007/s00376-012-2167-8
[20]	Lu WANG, Xueshun SHEN, Juanjuan LIU, Bin WANG, 2020: Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 817-831. doi: 10.1007/s00376-020-9262-z

PDF

Get Citation+

Export:

Article Metrics

Article Views: 1189 Times

PDF downloads: 1154 Times

Cited by: Times

Proportional views

Manuscript History

Manuscript received: 10 October 2000

Manuscript revised: 10 October 2000

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

A New Lagrangian-Eulerian Coupling Model System

1. Business Management Department; Electricity Generating Authority of Thailand; 53 Charan Sanit Wong Road. Bang Krui. Nonthaburi 11130. Thailand,LAPC; Institute of Atmospheric Physics（IAP）,Chinese Academy of Sciences; Beijing,King Mongkut`s University of Technology Thouburi, 91 Pracha-Utit Road, Bangmod, Thongkru, Bangkok 10140,Thailand

Manuscript received: 2000-10-10
Manuscript revised: 2000-10-10

Abstract: A new Lagrangiann-Eulerian coupling model system is developed to study regional air quality. The system consists of mesoscale dynamical meteorological model (MM), Monte-Carlo model (MCM), parameterized model on planetary boundary layer (PBL) turbulent statistics, dry and wet removal model, and Eulerian nonlinear chemical model (ENCM). The physical, chemical and biological processes on air pollutants are considered comprehensively. 3-D distribution laws for acidic gaseous pollutants (SO2 and NOx) emitted by Thai Mae Moh Power Plant and the secondary pollutants are studied in detail. The results simulated by the coupling model system are in good agreement with observational concentration data.

Keywords: