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基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究

谢飞 田文寿 郑飞 张健恺 陆进鹏

谢飞, 田文寿, 郑飞, 等. 2021. 基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究[J]. 大气科学, 46(X): 1−19 doi: 10.3878/j.issn.1006-9895.2104.21014
引用本文: 谢飞, 田文寿, 郑飞, 等. 2021. 基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究[J]. 大气科学, 46(X): 1−19 doi: 10.3878/j.issn.1006-9895.2104.21014
XIE Fei, TIAN Wenshou, ZHENG Fei, et al. 2021. Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(X): 1−19 doi: 10.3878/j.issn.1006-9895.2104.21014
Citation: XIE Fei, TIAN Wenshou, ZHENG Fei, et al. 2021. Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(X): 1−19 doi: 10.3878/j.issn.1006-9895.2104.21014

基于DART +WACCM模式搭建的平流层同化、天气预报和气候预测模型研究

doi: 10.3878/j.issn.1006-9895.2104.21014
基金项目: 国家自然科学基金项目42122037、41975047
详细信息
    作者简介:

    谢飞,男,1983年出生,博士,主要从事平流层对流层相互作用方面的研究。E-mail: xiefei@bnu.edu.cn

    通讯作者:

    田文寿,E-mail: wstian@lzu.edu.cn

  • 中图分类号: P456.7

Stratospheric Assimilation, Weather Forecast, and Climate Prediction Model Based on Data Assimilation Research Testbed and Whole Atmosphere Community Climate Model

Funds: National Natural Science Foundation of China (Grants: 42122037, 41975047)
  • 摘要: 本论文基于Whole Atmosphere Community Climate Model模式最新版本WACCM6和Data Assimilation Research TestBed(DART)同化工具最新版本Manhattan,开发了中高层大气温度、臭氧和水汽卫星资料的同化接口,搭建了一个包含完整平流层过程的数值同化、天气预报和短期气候预测模型(此后简称模型);本模型对2020年3~4月平流层大气变化进行了同化观测资料的模拟,并以同化试验输出的分析场作为初值,对5~6月的平流层大气进行了0~30天天气尺度预报以及31~60天短期气候尺度预测的回报试验。结果表明:本模型能较好地重现2020年3、4月北极平流层出现的大规模臭氧损耗事件随时间的演变特征,模拟结果和Microwave Limb Sounder(MLS)卫星观测结果很接近;而未进行同化的模拟试验,虽然可以模拟出北极臭氧损耗现象,但是模拟的臭氧损耗规模相比MLS卫星观测结果要低很多;利用同化试验4月末输出的分析场作为初值,预报的5月北极平流层臭氧体积混合比(ppmv)变化与MLS卫星观测值的差值小于0.5,预测的6月北极平流层臭氧变化只在10~30 hPa之间的区域,与观测之间的差异达到了1 ppmv。本模型不但改善了北极平流层化学成分变化的模拟,也显著地提升了北极平流层温度和环流的模拟。本模型同化模拟的3~4月、预报预测的5~6月北极平流层温度和纬向风变化与Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2)再分析资料结果具有很好的一致性,仅在北极平流层顶部,预报预测的温度和纬向风分别与再分析资料之间的均方根误差(RMSE)约为3 K和4 m s−1。未进行同化的试验模拟的3~4月、预报预测的5~6月北极平流层的温度和纬向风与MERRA2再分析资料之间的RMSE在大部分区域都达到6 K及5 m s−1以上。从全球范围来看,本模型对平流层中低层模拟性能改善最为显著,其预报预测结果与观测值之间的差异,比未进行同化试验的结果,减少了50%以上。
  • 图  1  第一组试验(同化SABER资料的试验,见表1)模拟的2020年(a)3月1日,(b)3月15日,(c)3月30日,(d)4月15日和(e)4月30日TCO(单位:DU)分布

    Figure  1.  First set of experiments (Table 1) and simulated tropospheric column ozone (TCO, units: DU) distributions for (a) March 1, (b) March 15, (c) March 30, (d) April 15, and (e) April 30, 2020

    图  2  MERRA2再分析资料中,过去30年(1990~2020年)60oN~90oN平均的TCO(单位:DU)月变化曲线。黑线为1990~2019年TCO月变化的平均结果,阴影区域代表1990~2019年TCO的月变化范围,蓝线代表2020年TCO月变化曲线

    Figure  2.  MERRA2 reanalysis of monthly TCO (units: DU) changes averaged over 60°–90° N for the past 30 years (1990–2020). The black line indicates the average of the monthly TCO changes from 1990–2019, while the shaded area represents the range of TCO changes from 1990–2019. The blue line indicates the monthly TCO change for 2020

    图  3  第二组试验(未进行同化的试验,见表1)模拟的2020年(a)3月1日,(b)3月15日,(c)3月30日,(d)4月15日和(e)4月30日TCO(单位:DU)分布

    Figure  3.  Second set of experiments (see Table 1) simulated TCO (units: DU) distributions for (a) March 1, (b) March 15, (c) March 30, (d) April 15, and (e) April 30, 2020

    图  4  (a)第一组试验(同化SABER资料的试验,见表1)模拟的2020年3、4月北极地区平均的臭氧含量变化。(b)第二组试验(未进行同化的试验,见表1)模拟的2020年3、4月北极地区平均的臭氧含量变化。(c)为(a)中的臭氧含量变化与过去20年北极地区平均的臭氧变化的气候态的差值。(d)为(a)中的臭氧含量变化与MLS资料中的北极地区平均的臭氧含量变化的差值。单位:ppmv

    Figure  4.  (a) Simulated changes in Arctic-averaged ozone from the first set of experiments in March and April 2020 (Table 1). (b) Simulated changes in Arctic-averaged ozone from the second set of experiments in March and April 2020 (Table 1). (c) Difference between the ozone changes in (a) and Arctic-averaged ozone change over the past decade. (d) Difference between the ozone changes in (a) and the Arctic-averaged ozone changes based on MLS data. Units: ppmv

    图  5  (a)第三组试验(以第一组同化试验输出的分析场为初值的试验,见表1)预报的5月和预测的6月的北极地区平均的臭氧含量变化。(b)为(a)图中的臭氧含量变化与MLS资料中臭氧含量变化的差值。单位:ppmv

    Figure  5.  (a) Arctic-averaged ozone changes forecasted and predicted by the third set of experiments (Table 1) for May and June. (b) Difference between ozone changes in (a) and those in the MLS data. Units: ppmv

    图  6  (a)第一组试验(同化SABER资料的试验,见表1)模拟的2020年3、4月北极地区平均的温度变化。(b)第二组试验(未进行同化的试验,见表1)模拟的2020年3、4月北极地区平均的温度变化。(c)MERRA2再分析资料中的2020年3、4月北极地区平均的温度变化。(d)为(a)与(c)的差值。(e)为(b)与(c)的差值。单位:K

    Figure  6.  (a) Arctic-averaged temperature change in March and April 2020 simulated in the first set of experiments (Table 1). (b) Arctic-averaged temperature change in March and April 2020 simulated in the second set of experiments (Table 1). (c) MERRA2 reanalysis for Arctic-averaged temperature change in March and April 2020. (d) Difference between (a) and (c). (e) Difference between (b) and (c). Units: K

    图  7  图6类似,但是为纬向风变化。单位:m s−1

    Figure  7.  Same as Fig. 6, but for zonal wind changes. Units: m s−1

    图  8  (a)2020年4月30日北极地区(60°~90°N)平均的温度垂直曲线。黑线基于MERRA2资料,蓝线基于第一组试验(同化SABER资料的试验)资料,红线基于第二组试验(未进行同化的试验)资料。(b)中蓝线为(a)中蓝线与黑线之差,代表第一组试验结果与MERRA2的差值,红线为(a)中红线线与黑线之差,代表第二组试验结果与MERRA2的差值。(c)、(d)与(a)、(b)类似,但是为纬向风的变化

    Figure  8.  (a) Average temperature vertical curve for the Arctic region (60°–90° N) for April 30, 2020. The black line corresponds to MERRA2, the blue line to the first set of experiments, and the red line to the second set of experiments. (b) Difference between the first set of experiments and the MERRA2 (blue line) and difference between the second set of experiments and the MERRA2 (red line). (c) and (d) are similar to (a) and (b), except for zonal wind changes

    图  9  (a)第三组试验(以第一组同化试验输出的分析场为初值的试验,见表1)预报的5月和预测的6月的北极地区平均的温度变化。(b)第四组试验(以第二组未进行同化的试验输出的分析场为初值的试验,见表1)预报的5月和预测6月的北极地区平均的温度变化。(c)MERRA2再分析资料中的2020年5、6月北极地区平均的温度变化。(d)为(a)与(c)的差值。(e)为(b)与(c)的差值。单位:K

    Figure  9.  (a) Forecasted and predicted changes in Arctic-averaged temperature for May and June by the third set of experiments (Table 1). (b) Forecasted and predicted changes in Arctic-averaged temperature for May and June by the fourth set of experiments (Table 1). (c) Arctic-averaged temperature changes for May and June 2020 based on MERRA2 reanalysis data. (d) Difference between (a) and (c). (e) Difference between (b) and (c). Unis: K

    图  10  图9,但为纬向风变化。单位:m s−1

    Figure  10.  Same as Fig. 9, but for zonal wind changes. Units: m s−1

    图  11  (a)和(c)分别为第一组试验(同化SABER资料的试验,见表1)模拟的2020年3、4月平流层温度和纬向风与MERRA2再分析资料之间的RMSE。(b)和(d)分别为第二组试验(未进行同化的试验,见表1)模拟的2020年3、4月平流层温度和纬向风与MERRA2再分析资料之间的RMSE

    Figure  11.  (a) Stratospheric temperature and (c) wind RMSEs of Marchand April 2020 between the first set of experiments (Table 1) and the MERRA2 reanalysis data; (b) stratospheric temperature and (d) wind RMSEs of March and April 2020 between the second set of experiments (Table 1) and the MERRA2 reanalysis data

    图  12  第三组试验(以第一组同化试验输出的分析场为初值的试验,见表1)输出的平流层温度5月(a)0~3日预报,(c)4~15日预报和(e)16~30日预报和(g)6月短期气候预测结果与MERRA2再分析资料之间的RMSE。(b)、(d)、(f)和(h)分别为第四组试验(以第二组未进行同化的试验输出的分析场为初值的试验,见表1)输出的平流层温度5月(b)0~3日预报,(d)4~15日预报和(f)16~30日预报和(h)6月短期气候预测结果与MERRA2再分析资料之间的RMSE

    Figure  12.  Stratospheric temperature RMSEs between the (a) 0–3 days forecast, (c) 4–15 days forecast, (e) 16–30 days forecast, and (g) short-term climate prediction from the third set of experiments (Table 1) and the MERRA2 reanalysis data; stratospheric temperature RMSEs between the (b) 0–3 days forecast, (d) 4–15 days forecast, (f) 16–30 days forecast, and (h) short-term climate prediction from the fourth set of experiments (Table 1) and the MERRA2 reanalysis data

    图  13  图12,但为纬向风变化

    Figure  13.  Same as Fig. 12, but for zonal wind changes

    表  1  四组试验设计

    Table  1.   Design of experiments

    模拟时段同化海温,海冰试验初值
    第一组2020年3月1日至4月30日SABER温度、臭氧、水汽Hadley观测资料WACCM6默认设置输入
    第二组2020年3月1日至4月30日无同化Hadley观测资料WACCM6默认设置输入
    第三组2020年5月1日至6月30日无同化CFSv2海洋模式预测资料第一组试验输出的4月30日分析场
    第四组2020年5月1日至6月30日无同化CFSv2海洋模式预测资料第二组试验输出的4月30日分析场
    下载: 导出CSV
  • [1] Anderson J L. 2003. A local least squares framework for ensemble filtering [J]. Mon. Wea. Rev., 131(4): 634−642. doi: 10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2
    [2] Anderson J L. 2010. A non-Gaussian ensemble filter update for data assimilation [J]. Mon. Wea. Rev., 138(11): 4186−4198. doi: 10.1175/2010MWR3253.1
    [3] Baldwin M P, Dunkerton T J. 2001. Stratospheric harbingers of anomalous weather regimes [J]. Science, 294(5542): 581−584. doi: 10.1126/science.1063315
    [4] 卞建春, 严仁嫦, 陈洪滨. 2011. 亚洲夏季风是低层污染物进入平流层的重要途径 [J]. 大气科学, 35(5): 897−902. doi: 10.3878/j.issn.1006-9895.2011.05.09

    Bian Jianchun, Yan Renchang, Chen Hongbin. 2011. Tropospheric pollutant transport to the stratosphere by Asian summer monsoon [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 35(5): 897−902. doi: 10.3878/j.issn.1006-9895.2011.05.09
    [5] 陈洪滨, 卞建春, 吕达仁. 2006. 上对流层—下平流层交换过程研究的进展与展望 [J]. 大气科学, 30(5): 813−820. doi: 10.3878/j.issn.1006-9895.2006.05.10

    Chen Hongbin, Bian Jianchun, Lü Daren. 2006. Advances and prospects in the study of stratosphere-troposphere exchange [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 30(5): 813−820. doi: 10.3878/j.issn.1006-9895.2006.05.10
    [6] Chen Z Y, Chen H B, Chen W, et al. 2008. Advances in the researches in middle and upper atmosphere in 2006–2008 [J]. Chin. J. Space Sci., 28(5): 412−423.
    [7] 陈月娟, 易明建, 毕云, 等. 2009. 平流层微量气体变化趋势的研究 [J]. 地球科学进展, 24(3): 308−319. doi: 10.3321/j.issn:1001-8166.2009.03.009

    Chen Yuejuan, Yi Mingjian, Bi Yun, et al. 2009. A study of the trends of the trace gases in stratosphere [J]. Advances in Earth Science (in Chinese), 24(3): 308−319. doi: 10.3321/j.issn:1001-8166.2009.03.009
    [8] Chen Q L, Li Z, Fan G Z, et al. 2011. Indications of stratospheric anomalies in the freezing rain and snow disaster in South China 2008 [J]. Sci. China Earth Sci., 54(8): 1248−1256. doi: 10.1007/s11430-011-4192-3
    [9] Coy L, Eckermann S, Hoppel K. 2009. Planetary wave breaking and tropospheric forcing as seen in the stratospheric sudden warming of 2006 [J]. J. Atmos. Sci., 66(2): 495−507. doi: 10.1175/2008JAS2784.1
    [10] Elbern H, Schwinger J, Botchorishvili R. 2010. Chemical state estimation for the middle atmosphere by four-dimensional variational data assimilation: System configuration [J]. J. Geophys. Res.: Atmos., 115(D6): D06302. doi: 10.1029/2009JD011953
    [11] Errera Q, Daerden F, Chabrillat S, et al. 2008. 4D-Var assimilation of MIPAS chemical observations: Ozone and nitrogen dioxide analyses [J]. Atmos. Chem. Phys., 8(20): 6169−6187. doi: 10.5194/acp-8-6169-2008
    [12] Garcia R R, Marsh D R, Kinnison D E, et al. 2007. Simulation of secular trends in the middle atmosphere, 1950–2003 [J]. J. Geophys. Res. :Atmos., 112(D9): D09301. doi: 10.1029/2006JD007485
    [13] 胡永云. 2006. 平流层极地臭氧损耗影响对流层气候的研究进展 [J]. 北京大学学报(自然科学版), 42(5): 561−568. doi: 10.3321/j.issn:0479-8023.2006.05.001

    Hu Yongyun. 2006. Possible impact of stratospheric polar ozone depletion on tropospheric climate [J]. Acta Scientiarum Naturalium Universitatis Pekinensis (in Chinese), 42(5): 561−568. doi: 10.3321/j.issn:0479-8023.2006.05.001
    [14] Hu Y Y. 2020. The very unusual polar stratosphere in 2019–2020 [J]. Sci. Bull., 65(21): 1775−1777. doi: 10.1016/j.scib.2020.07.011
    [15] Ivy D J, Solomon S, Calvo N, et al. 2017. Observed connections of Arctic stratospheric ozone extremes to Northern Hemisphere surface climate [J]. Environ. Res. Lett., 12(2): 024004. doi: 10.1088/1748-9326/aa57a4
    [16] Jackson D R. 2007. Assimilation of EOS MLS ozone observations in the Met Office data-assimilation system [J]. Quart. J. Roy. Meteor. Soc., 133(628): 1771−1788. doi: 10.1002/qj.140
    [17] 敬文琪, 王业桂, 崔园园, 等. 2019. 基于WACCM+DART的临近空间SABER和MLS臭氧观测同化试验研究 [J]. 大气科学, 43(2): 233−250. doi: 10.3878/j.issn.1006-9895.1803.17184

    Jing Wenqi, Wang Yegui, Cui Yuanyuan, et al. 2019. Assimilation of near space ozone data from SABER and MLS observations into the whole atmosphere community climate model and data assimilation research test-bed [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(2): 233−250. doi: 10.3878/j.issn.1006-9895.1803.17184
    [18] Kinnison D E, Brasseur G P, Walters S, et al. 2007. Sensitivity of chemical tracers to meteorological parameters in the MOZART-3 chemical transport model [J]. J. Geophys. Res.: Atmos., 112(D20): D20302. doi: 10.1029/2006JD007879
    [19] Li T, She C Y, Liu H L, et al. 2007. Sodium lidar-observed strong inertia-gravity wave activities in the mesopause region over Fort Collins, Colorado (41°N, 105°W) [J]. J. Geophys. Res.: Atmos., 112(D22): D22104. doi: 10.1029/2007JD008681
    [20] Lin S J. 2004. A “Vertically Lagrangian” finite-volume dynamical core for global models [J]. Mon. Wea. Rev., 132(10): 2293−2307. doi: 10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2
    [21] 刘毅, 刘传熙. 2009. 利用WACCM-3模式对平流层动力、热力场及微量化学成分季节变化的数值模拟研究 [J]. 空间科学学报, 29(6): 580−590. doi: 10.11728/cjss2009.06.580

    Liu Yi, Liu Chuanxi. 2009. Simulation: Studies on seasonal variations of the stratospheric dynamics and trace gases using coupled chemistry-climate model WACCM-3 [J]. Chinese Journal of Space Science (in Chinese), 29(6): 580−590. doi: 10.11728/cjss2009.06.580
    [22] Livesey N J. 2015. Earth Observing System (EOS) Aura Microwave Limb Sounder (MLS) version 4.2 x level 2 data quality and description document, 91109–8099, version 4.2 x [R]. Jet Propulsion Lab., Calif. Insti. of Tech., Pasadena, Calif.
    [23] 吕达仁, 陈洪滨. 2003. 平流层和中层大气研究的进展 [J]. 大气科学, 27(4): 750−769. doi: 10.3878/j.issn.1006-9895.2003.04.21

    Lü Daren, Chen Hongbin. 2003. Advances in middle atmosphere physics research [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 27(4): 750−769. doi: 10.3878/j.issn.1006-9895.2003.04.21
    [24] McCormack J P, Coy L, Hoppel K W. 2009. Evolution of the quasi 2-day wave during January 2006 [J]. J. Geophys. Res.:Atmos., 114(D20): D20115. doi: 10.1029/2009JD012239
    [25] Pedatella N M, Raeder K, Anderson J L, et al. 2013. Application of data assimilation in the Whole Atmosphere Community Climate Model to the study of day-to-day variability in the middle and upper atmosphere [J]. Geophys. Res. Lett., 40(16): 4469−4474. doi: 10.1002/grl.50884
    [26] Pierce R B, Schaack T, Al-Saadi J A, et al. 2007. Chemical data assimilation estimates of continental U. S. ozone and nitrogen budgets during the intercontinental chemical transport experiment-North America [J]. J. Geophys. Res.: Atmos., 112(D12): D12S21. doi: 10.1029/2006JD007722
    [27] Polavarapu S, Ren S Z, Rochon Y, et al. 2005. Data assimilation with the Canadian middle atmosphere model [J]. Atmos. Ocean, 43(1): 77−100. doi: 10.3137/ao.430105
    [28] Ren R C, Hu J G. 2014. An emerging precursor signal in the stratosphere in recent decades for the Indian summer monsoon onset [J]. Geophys. Res. Lett., 41(20): 7391−7396. doi: 10.1002/2014GL061633
    [29] Ren S Z, Polavarapu S, Beagley S R, et al. 2011. The impact of gravity wave drag on mesospheric analyses of the 2006 stratospheric major warming [J]. J. Geophys. Res.: Atmos., 116(D19): D19116. doi: 10.1029/2011JD015943
    [30] Sassi F, Liu H L, Ma J, et al. 2013. The lower thermosphere during the Northern Hemisphere winter of 2009: A modeling study using high-altitude data assimilation products in WACCM-X [J]. J. Geophys. Res. :Atmos., 118(16): 8954−8968. doi: 10.1002/jgrd.50632
    [31] Siskind D E, Stevens M H, Hervig M, et al. 2011. Consequences of recent Southern Hemisphere winter variability on polar mesospheric clouds [J]. J. Atmos. Sol. Terr. Phys., 73(13): 2013−2021. doi: 10.1016/j.jastp.2011.06.014
    [32] 田文寿, 田红瑛, 商林, 等. 2011. 热带平流层与对流层之间相互作用的研究进展 [J]. 热带气象学报, 27(5): 765−774. doi: 10.3969/j.issn.1004-4965.2011.05.00

    Tian Wenshou, Tian Hongying, Shang Lin, et al. 2011. Advances in interactions between tropical stratosphere and troposphere [J]. Journal of Tropical Meteorology (in Chinese), 27(5): 765−774. doi: 10.3969/j.issn.1004-4965.2011.05.00
    [33] Wang H, Fuller-Rowell T J, Akmaev R A, et al. 2011. First simulations with a whole atmosphere data assimilation and forecast system: The January 2009 major sudden stratospheric warming [J]. J. Geophys. Res. :Space Phys., 116(A12): A12321. doi: 10.1029/2011JA017081
    [34] Witze A. 2020. Rare ozone hole opens over Arctic — and it’s big [J]. Nature, 580(7801): 18−19. doi: 10.1038/d41586-020-00904-w
    [35] 肖存英, 胡雄, 杨钧烽, 等. 2017. 临近空间Aura/MLS卫星数据同化技术及其在数值预报中的应用[C]//第四届高分辨率对地观测学术年会论文集. 武汉, 1–17

    Xiao Cunying, Hu Xiong, Yang Junfeng, et al. 2017. Near-space Aura/MLS satellite data assimilation technology and its application in numerical forecast [C]//Proceedings of the Fourth Annual Conference on High-resolution Earth Observation (in Chinese). Wuhan, 1–17.
    [36] Xie F, Li J P, Tian W S, et al. 2016. A connection from Arctic stratospheric ozone to El Niño–Southern oscillation [J]. Environ. Res. Lett., 11(12): 124026. doi: 10.1088/1748-9326/11/12/124026
    [37] Xie F, Li J P, Zhang J K, et al. 2017. Variations in North Pacific Sea surface temperature caused by arctic stratospheric ozone anomalies [J]. Environ. Res. Lett., 12(11): 114023. doi: 10.1088/1748-9326/aa9005
    [38] Xu J Y, Li Q Z, Yue J, et al. 2015. Concentric gravity waves over Northern China observed by an airglow imager network and satellites [J]. J. Geophys. Res.: Atmos., 120(21): 11058−11078. doi: 10.1002/2015JD023786
    [39] Yi F, Zhang S D, Zeng H J, et al. 2002. Lidar observations of sporadic Na layers over Wuhan (30.5°N, 114.4°E) [J]. Geophys. Res. Lett., 29(9): 59-1–59-4. doi:10.1029/2001GL014353
    [40] Zheng X D, Zhou X J, Tang J, et al. 2004. A meteorological analysis on a low tropospheric ozone event over Xining, North western China on 26–27 July 1996 [J]. Atmos. Environ., 38(2): 261−271. doi: 10.1016/j.atmosenv.2003.09.063
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  • 收稿日期:  2021-01-25
  • 录用日期:  2021-10-12
  • 网络出版日期:  2021-12-09

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