大落差山地边界层方案的订正及臭氧模拟

罗才桂, 樊晋, 刘梦璇, 颜廷昱, 苏有琦, 李茂善, 张小玲

中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5357-5366.

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (10) : 5357-5366.
大气污染与控制

大落差山地边界层方案的订正及臭氧模拟

  • 罗才桂1,2, 樊晋1,2,3, 刘梦璇1,2, 颜廷昱4, 苏有琦1,2, 李茂善1,2, 张小玲1,2
作者信息 +

Modification of the boundary layer scheme in complex terrain and evaluation of ozone simulation

  • LUO Cai-gui1,2, FAN Jin1,2,3, LIU Meng-xuan1,2, YAN Ting-yu4, SU You-qi1,2, LI Mao-shan1,2, ZHANG Xiao-ling1,2
Author information +
文章历史 +

摘要

通过在大落差山地开展边界层湍流脉动量观测,基于观测数据对一阶非局地边界层方案进行本地适用性订正,并评估订正后对臭氧的模拟性能.结果表明: 订正方案显著缓解了区域地面臭氧浓度的低估问题,将模拟偏差由−5.14×10-9降低至−2.32×10-9,其中臭氧平流与化学过程为改善的主要驱动因素.通过修改YSU (Yonsei University)边界层方案中的最小湍流扩散系数,不同下垫面高空臭氧浓度呈现出平流与垂直混合过程的相反贡献特征,揭示了大落差山地与毗邻平原在不同高度层臭氧输送机制的显著差异.研究成果为复杂地形区域边界层参数化优化与臭氧模拟精度提升提供了理论依据与方法支撑.

Abstract

The complex topographical conditions from the eastern Tibetan Plateau to the Chengdu Plain pose significant challenges for traditional boundary layer parameterization schemes, which in turn affects the accuracy of regional air quality models. To better understand these challenges, boundary layer turbulence flux observations were conducted in mountainous regions with substantial elevation variations. Based on these observations, a first-order nonlocal boundary layer scheme was refined for better applicability to local conditions, and its impact on ozone (O3) simulations was assessed. The results show that the modified scheme improved WRF-Chem’s simulation of O3 advection and chemical processes, reducing the surface O3 concentration bias from -5.14 ×10-9 to -2.32×10-9, demonstrating improved model performance. Moreover, differences in O3 advection and vertical mixing highlighted distinct transport mechanisms between the mountainous region and the adjacent plains, emphasizing the influence of complex terrain on regional air quality.

关键词

边界层参数化方案 / 臭氧模拟 / WRF-Chem / 最小湍流扩散系数

Key words

Yonsei University Scheme (YSU) / ozone / WRF-Chem / Kzmin

引用本文

导出引用
罗才桂, 樊晋, 刘梦璇, 颜廷昱, 苏有琦, 李茂善, 张小玲. 大落差山地边界层方案的订正及臭氧模拟[J]. 中国环境科学. 2025, 45(10): 5357-5366
LUO Cai-gui, FAN Jin, LIU Meng-xuan, YAN Ting-yu, SU You-qi, LI Mao-shan, ZHANG Xiao-ling. Modification of the boundary layer scheme in complex terrain and evaluation of ozone simulation[J]. China Environmental Science. 2025, 45(10): 5357-5366
中图分类号: X511   

参考文献

[1] Georgiou G K, Christoudias T, Proestos Y, et al. Evaluation of WRF/Chem model (v3. 9.1. 1) real-time air quality forecasts over the Eastern Mediterranean [J]. Geoscientific Model Development Discussions, 2022,2022:1-23.
[2] Guo W, Chen Q, Yang Y, et al. Investigating the mechanism of morning ozone concentration peaks in a petrochemical industrial city [J]. Atmospheric Environment, 2022,270:118897.
[3] Shu Z, Liu Y, Zhao T, et al. Elevated 3D structures of PM2.5 and impact of complex terrain-forcing circulations on heavy haze pollution over Sichuan Basin, China [J]. Atmospheric Chemistry and Physics, 2021,21(11): 9253-9268.
[4] Shu Z, Zhao T, Chen Y, et al. Terrain effect on atmospheric process in seasonal ozone variation over the Sichuan Basin, Southwest China [J]. Environmental Pollution, 2023,338:122622.
[5] Zhang L, Guo X, Zhao T, et al. A modelling study of the terrain effects on haze pollution in the Sichuan Basin [J]. Atmospheric Environment, 2019,196:77-85.
[6] Jia W, Zhang X. Impact of modified turbulent diffusion of PM2.5 aerosol in WRF-Chem simulations in eastern China [J]. Atmospheric Chemistry and Physics, 2021,21(22):16827-16841.
[7] Ko K, Cho S, Rao R R. Machine-learning-based near-surface ozone forecasting model with planetary boundary layer information [J]. Sensors, 2022,22(20):7864.
[8] 张小玲,卢宁生,雷 雨,等.成都平原城市群夏季臭氧污染特征与成因分析 [C]//第二十五届大气污染防治技术研讨会论文集, 2021. Zhang Xiaoling, Lu Ningsheng, Lei Yu, et al. Characteristics and causes of summer ozone pollution in the Chengdu Plain urban agglomeration [C]//Proceedings of the 25th Symposium on Air Pollution Prevention and Control Technology, 2021.
[9] Hong S Y. A new stable boundary‐layer mixing scheme and its impact on the simulated East Asian summer monsoon [J]. Quarterly Journal of the Royal Meteorological Society, 2010,136(651):1481- 1496.
[10] Li X, Rappenglueck B. A study of model nighttime ozone bias in air quality modeling [J]. Atmospheric Environment, 2018,195: 210-228.
[11] Ryzhakova N, Rogova N, Pokrovskaya E, et al. Influence of natural and climatic conditions on the values of the vertical turbulent diffusion coefficient for long observation periods [J]. Izvestiya, Atmospheric and Oceanic Physics, 2022,58(6):553-559.
[12] 张碧辉,刘树华,马雁军.MYJ和YSU方案对WRF边界层气象要素模拟的影响 [J]. 地球物理学报, 2012,55(7):2239-2248. Zhang B H, Liu S H, Liu H P, et al. The effect of MYJ and YSU schemes on the simulation of boundary layer meteorological factors of WRF [J]. Chinese Journal of Geophysics, 2012,55(7):2239-2248.
[13] 芦延廷,赵秀娟,唐贵谦,等.边界层方案与垂直混合对京津冀地区O3模拟的影响研究 [J]. 中国环境科学, 2022,42(12):5459-5471. Lu Y T, Zhao X J, Tang G Q, et al. The study on the impact of boundary layer schemes on O3simulations in the Beijing-Tianjin- Hebei region. [J]. China Environmental Science, 2022,42(12):5459- 5471.
[14] Du Q, Zhao C, Zhang M, et al. Modeling diurnal variation of surface PM 2.5concentrations over East China with WRF-Chem: Impacts from boundary-layer mixing and anthropogenic emission [J]. Atmospheric Chemistry and Physics, 2020,20(5):2839-2863.
[15] Wang A, Li Y, Zhao C, et al. Influence of different boundary layer 420 schemes on PM2.5 concentration simulation in Nanjing [J]. China Environmental Science, 2021,41(7):2977-2992.
[16] Xiao D, Wen L, Zhang Y, et al. Natural gas accumulation in the basin–mountain transition zone, northwestern Sichuan Basin, China [J]. Marine and Petroleum Geology, 2021,133:105305.
[17] Li M, Liu H, Geng G, et al. Anthropogenic emission inventories in China: a review [J]. National Science Review, 2017,4(6):834-866.
[18] Li M, Zhang Q, Kurokawa J I, et al. MIX: A mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP [J]. Atmospheric Chemistry and Physics, 2017,17(2):935-963.
[19] Sindelarova K, Granier C, Bouarar I, et al. Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30years [J]. Atmospheric Chemistry and Physics, 2014,14(17):9317- 9341.
[20] Du Y, Xu T, Che Y, et al. Uncertainty quantification of WRF model for rainfall prediction over the Sichuan basin, China [J]. Atmosphere, 2022,13(5):838.
[21] Tian J, Liu R, Ding L, et al. Evaluation of the WRF physical parameterisations for Typhoon rainstorm simulation in southeast coast of China [J]. Atmospheric Research, 2021,247:105130.
[22] Lekhadiya H, Jana R. Assimilation of INSAT-3D satellite data in WRF model [J]. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2020,90:557-564.
[23] Balzarini A, Pirovano G, Honzak L, et al. WRF-Chem model sensitivity to chemical mechanisms choice in reconstructing aerosol optical properties [J]. Atmospheric Environment, 2015,115:604-619.
[24] Thomas A, Huff A K, Hu X M, et al. Quantifying uncertainties of ground-level ozone within WRF-Chem simulations in the mid-Atlantic region of the United States as a response to variability [J]. Journal of Advances in Modeling Earth Systems, 2019,11(4):1100-1116.
[25] Yang J, Kang S, Ji Z. Sensitivity analysis of chemical mechanisms in the WRF-Chem model in reconstructing aerosol concentrations and optical properties in the Tibetan Plateau [J]. Aerosol and Air Quality Research, 2018,18(2):505-521.
[26] Bucaram C J, Bowman F M. Wrf-chem modeling of summertime air pollution in the northern great plains: Chemistry and aerosol mechanism intercomparison [J]. Atmosphere, 2021,12(9):1121.
[27] Mar K A, Ojha N, Pozzer A, et al. Ozone air quality simulations with WRF-Chem (v3. 5.1) over Europe: model evaluation and chemical mechanism comparison [J]. Geoscientific Model Development, 2016, 9(10):3699-3728.
[28] Surussavadee C. Evaluation of WRF near-surface wind simulations in tropics employing different planetary boundary layer schemes; proceedings of the 2017 8th International renewable energy congress (IREC), F, 2017 [C]. IEEE.
[29] Wei W, Li Y, Ren Y, et al. Sensitivity of summer ozone to precursor emission change over Beijing during 2010~2015: A WRF-Chem modeling study [J]. Atmospheric Environment, 2019,218:116984.
[30] Morichetti M, Madronich S, Passerini G, et al. Comparison and evaluation of updates to WRF-Chem (v3. 9) biogenic emissions using MEGAN [J]. Geoscientific Model Development, 2022,15(16):6311- 6339.
[31] Hong S-Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes [J]. Monthly weather review, 2006,134(9):2318-2341.
[32] Hong S-Y, Pan H-L. Nonlocal boundary layer vertical diffusion in a medium-range forecast model [J]. Monthly weather review, 1996, 124(10):2322.
[33] Emery C, Liu Z, Russell A G, et al. Recommendations on statistics and benchmarks to assess photochemical model performance [J]. Journal of the Air & Waste Management Association, 2017,67(5):582-598.
[34] Hurley P J, Blockley A, Rayner K. Verification of a prognostic meteorological and air pollution model for year-long predictions in the Kwinana industrial region of Western Australia [J]. Atmospheric Environment, 2001,35(10):1871-1880.
[35] Willmott C J, Ackleson S G, Davis R E, et al. Statistics for the evaluation and comparison of models [J]. Journal of Geophysical Research: Oceans, 1985,90(C5):8995-9005.
[36] Liu Y, Tang G, Liu B, et al. Decadal changes in ozone in the lower boundary layer over Beijing, China [J]. Atmospheric Environment, 2022,275:119018.

基金

国家自然科学基金资助项目(42105167);四川省留学回国人员科技活动项目(川人社函703);成都市科技项目(2023-YF09-00013-SN);四川省自然科学基金资助项目(2025ZNSFSC0332);国家重点研发计划项目(2023YFC3709301)

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