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Volume 4 Issue 4

Oct.  1987

Article Contents

A PUFF MODEL REVISED BY MONTE-CARLO METHOD ON MESOSCALE RANGE


doi: 10.1007/BF02656745

  • A puff model is developed in this study, which simultaneously considers the Monte-Carlo technique, the time and space changes of atmospheric parameters, multiple continuity pollutant sources, linear chemical trans-formation and removal of pollutants, and the effect of complex terrain. The continuously observed turbulent statistical quantities, Lagrangian time scales, mesoscale flow field, and mixing layer depth in the PBL in the Dianchi area in China are directly put into the model, and the diurnal variations of air pollution are forecasted, which are dominated by such mesoscale local circulations as mountain and valley breeze, land and lake breeze, and city heat island (Kunming City). The results show that in the case of inputting the same data, they are in good agreement with the experimental data, as well as with the results of the three-dimensional advection-diffusion model (TD-ADM); the diurnal variation of mesoscale local circulation results in the obvious diurnal variation of mesoscale concentration distribution patterns; the Dianchi lake (appr. 300 km2) has a considerable effect on the distribution of air pollution in the area.
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    [2] Yifan ZHAO, Xindong PENG, Xiaohan LI, Siyuan CHEN, 2024: Improved Diurnal Cycle of Precipitation on Land in a Global Non-Hydrostatic Model Using a Revised NSAS Deep Convective Scheme, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3121-7
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    [4] Lin Wenshi, Fong Soikun, Wu Chisheng, Ku Chimeng, Wang Anyu, Yang Yan, 2000: A Simulating Study on Resolvable-Scale Microphysical Parameterization in a Mesoscale Model, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 487-502.  doi: 10.1007/s00376-000-0038-1
    [5] Seung-Jae LEE, E. Hugo BERBERY, Domingo ALCARAZ-SEGURA, 2013: Effect of Implementing Ecosystem Functional Type Data in a Mesoscale Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1373-1386.  doi: 10.1007/s00376-012-2143-3
    [6] Jieshun ZHU, Entcho DEMIROV, Ying ZHANG, and Ania POLOMSKA-HARLICK, 2014: Model Simulations of Mesoscale Eddies and Deep Convection in the Labrador Sea, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 743-754.  doi: 10.1007/s00376-013-3107-y
    [7] Zhao Qiang, Liu Shikuo, 1999: Simplification of Potential Vorcticity and Mesoscale Quasi-balanced Dynamics Model, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 304-313.  doi: 10.1007/BF02973090
    [8] XIA Zhiye, CHEN Hongbin, XU Lisheng, WANG Yongqian, 2015: Extended Range (10-30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1583-1591.  doi: 10.1007/s00376-015-4252-2
    [9] GAO Wenhua, ZHAO Fengsheng, HU Zhijin, FENG Xuan, 2011: A Two-Moment Bulk Microphysics Coupled with a Mesoscale Model WRF: Model Description and First Results, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1184-1200.  doi: 10.1007/s00376-010-0087-z
    [10] Haochen LI, Chen YU, Jiangjiang XIA, Yingchun WANG, Jiang ZHU, Pingwen ZHANG, 2019: A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1156-1170.  doi: 10.1007/s00376-019-9023-z
    [11] Runhua YANG, Jing GUO, Lars Peter RIISH?JGAARD, 2006: Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 33-44.  doi: 10.1007/s00376-006-0004-7
    [12] PENG Xindong, CHANG Yan, LI Xingliang, XIAO Feng, 2010: Application of the Characteristic CIP Method to a Shallow Water Model on the Sphere, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 728-740.  doi: 10.1007/s00376-009-9148-6
    [13] Bi Xunqiang, 1997: Parallel Computing of a Climate Model on the Dawn 1000 by Domain Decomposition Method, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 569-572.  doi: 10.1007/s00376-997-0075-0
    [14] 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
    [15] Wenbo XUE, Hui YU, Shengming TANG, Wei HUANG, 2024: Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3087-5
    [16] Shaowu BAO, Lian XIE, Sethu RAMAN, 2004: A Numerical Study of a TOGA-COARE Squall-Line Using a Coupled Mesoscale Atmosphere-Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 708-716.  doi: 10.1007/BF02916368
    [17] XUE Haile, SHEN Xueshun, CHOU Jifan, 2015: An Online Model Correction Method Based on an Inverse Problem: Part I——Model Error Estimation by Iteration, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1329-1340.  doi: 10.1007/s00376-015-4261-1
    [18] XUE Haile, SHEN Xueshun, CHOU Jifan, 2015: An Online Model Correction Method Based on an Inverse Problem: Part II——Systematic Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1493-1503.  doi: 10.1007/s00376-015-4262-0
    [19] Chuan GAO, Rong-Hua ZHANG, Xinrong WU, Jichang SUN, 2018: Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 410-422.  doi: 10.1007/s00376-017-7109-z
    [20] Bin MU, Juhui REN, Shijin YUAN, Rong-Hua ZHANG, Lei CHEN, Chuan GAO, 2019: The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1381-1392.  doi: 10.1007/s00376-019-9040-y

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Manuscript History

Manuscript received: 10 October 1987
Manuscript revised: 10 October 1987
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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A PUFF MODEL REVISED BY MONTE-CARLO METHOD ON MESOSCALE RANGE

  • 1. Institute of Atmospheric Physics, Academia Sinica,Institute of Atmospheric Physics, Academia Sinica

Abstract: A puff model is developed in this study, which simultaneously considers the Monte-Carlo technique, the time and space changes of atmospheric parameters, multiple continuity pollutant sources, linear chemical trans-formation and removal of pollutants, and the effect of complex terrain. The continuously observed turbulent statistical quantities, Lagrangian time scales, mesoscale flow field, and mixing layer depth in the PBL in the Dianchi area in China are directly put into the model, and the diurnal variations of air pollution are forecasted, which are dominated by such mesoscale local circulations as mountain and valley breeze, land and lake breeze, and city heat island (Kunming City). The results show that in the case of inputting the same data, they are in good agreement with the experimental data, as well as with the results of the three-dimensional advection-diffusion model (TD-ADM); the diurnal variation of mesoscale local circulation results in the obvious diurnal variation of mesoscale concentration distribution patterns; the Dianchi lake (appr. 300 km2) has a considerable effect on the distribution of air pollution in the area.

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