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公里尺度区域变分同化中引入大尺度约束的影响研究

王瑞春 龚建东 王皓

王瑞春, 龚建东, 王皓. 2021. 公里尺度区域变分同化中引入大尺度约束的影响研究[J]. 大气科学, 45(5): 1−16 doi: 10.3878/j.issn.1006-9895.2009.20176
引用本文: 王瑞春, 龚建东, 王皓. 2021. 公里尺度区域变分同化中引入大尺度约束的影响研究[J]. 大气科学, 45(5): 1−16 doi: 10.3878/j.issn.1006-9895.2009.20176
WANG Ruichun, GONG Jiandong, WANG Hao. 2021. Impact Studies of Introducing a Large-Scale Constraint into the Kilometer-Scale Regional Variational Data Assimilation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1−16 doi: 10.3878/j.issn.1006-9895.2009.20176
Citation: WANG Ruichun, GONG Jiandong, WANG Hao. 2021. Impact Studies of Introducing a Large-Scale Constraint into the Kilometer-Scale Regional Variational Data Assimilation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1−16 doi: 10.3878/j.issn.1006-9895.2009.20176

公里尺度区域变分同化中引入大尺度约束的影响研究

doi: 10.3878/j.issn.1006-9895.2009.20176
基金项目: 国家自然科学基金项目41705085, 国家重点研发计划项目2017YFC1502000, 中国气象局数值预报(GRAPES)发展专项
详细信息
    作者简介:

    王瑞春,男,1987年出生,高级工程师,主要从事大气资料同化研究。E-mail: wangrc@cma.gov.cn

  • 中图分类号: P456

Impact Studies of Introducing a Large-Scale Constraint into the Kilometer-Scale Regional Variational Data Assimilation

Funds: National Natural Science Foundation of China (Grant 41705085), National Key Research and Development Program of China (Grant 2017YFC1502000), China Meteorological Administration Special Fund for the Development of Numerical Weather Prediction (GRAPES)
  • 摘要: 公里尺度资料同化系统的框架设计和资料选择均侧重于中小尺度分析,常存在大尺度分析能力不足的问题。本研究在GRAPES(Global/Regional Assimilation and Prediction System)区域3 km三维变分同化目标泛函中增加大尺度约束,将全球系统的大尺度信息引入到分析框架中去,研究其对公里尺度同化预报的影响。一个月的数值试验结果表明,引入大尺度约束可以显著改进大尺度形势场的分析和预报,提高降水预报评分,减少2 m温度和10 m风场的分析预报误差。进一步的,定量降水敏感性试验结果表明,大尺度湿度场和温度场约束对于改进降水评分十分重要。这其中,湿度场约束对于减少降水空报以及提高短时临近降水的TS(Threat Score)评分重要,而温度场约束对于改进较长时效的TS降水评分重要。此外,在均引入大尺度约束的条件下,采用完全循环(一个月中间无冷启)方案运行的试验获得了与局部循环(每日冷启)相当的分析预报结果。这为GRAPES区域公里尺度系统采用完全循环方案,进一步简化流程,减少计算消耗奠定了很好的基础。
  • 图  1  本研究选取的模式范围示意图(黑色框线区域:20°N~40.1°N,100°E~127°E)

    Figure  1.  Model domain (within the black box: 20°–40.1°N, 100°–127°E) in this research

    图  2  2019年6月10日12:00(协调世界时,下同)700 hPa上,位于28°N的GFS(Global Forecast System)分析场(a)u、(b)v、(c)温度T以及(d)相对湿度μ在不同截断的低通滤波前后的对比示意图。黑色为原始场,红色为T106截断,深蓝色为T85截断,浅蓝色为T63截断

    Figure  2.  Difference before and after low-pass filtering of GFS fields at 700 hPa for (a) u, (b) v, (c) temperature T, and (d) relative humidity μ at 28°N, valid for 1200 UTC June 10, 2019. Black for the original field, red for T106 truncation, dark blue for T85 truncation, and light blue for T63 truncation

    图  3  大尺度环流信息(a)u、(b)v、(c)温度T以及(d)相对湿度μ的误差标准差的垂直廓线

    Figure  3.  Vertical profiles of standard deviation of the error of large-scale information: (a) u, (b) v, (c) temperature T, and (d) relative humidity μ

    图  4  数值试验同化预报循环流程示意图

    Figure  4.  Scheme of the analysis and forecast cycle for numerical experiments

    图  5  (a)降尺度冷启与(b)经4次同化暖启情形下GRAPES 3 km模式动能谱随预报时长的变化

    Figure  5.  Simulated kinetic energy spectra for different forecast lengths derived from the GRAPES 3-km model with (a) downscaling cold start and (b) warm start after four assimilation cycles

    图  6  数值试验采用的局部循环(上方)和完全循环(下方)方式示意图

    Figure  6.  Scheme of the partial cycle (top) and full cycle (bottom) for numerical experiments

    图  7  2019年6月10日至7月10日的12:00同化背景场中(a、d)u风、(b、e)温度T、(c、f)相对湿度μ与探空观测值相比的标准差(第一行)和Bias偏差(第二行)的整层平均廓线,试验设置见表1

    Figure  7.  Averaged standard deviation (STD, top line) and bias (bottom line) of the forecast background against the radiosonde observations verifying daily 1200 UTC from June 10, 2019, through July 10, 2019, for (a, d) u, (b,e) T, and (c, f) μ. Table 1 shows the experiment settings

    图  8  700 hPa上各试验的分析和预报结果与ERA5再分析资料相比的均方根误差:(a)u、(b)v、(c)T、(d)q。试验设置见表1

    Figure  8.  Horizontal averaged root mean square error (RMSE) for analysis and forecast results of different experiments compared with ERA5 reanalysis data at 700 hPa for (a) u, (b) v, (c) T, and (d) specific humidity q. Table 1 shows the experiment settings

    图  9  逐6 h检验的地面累积降水TS评分(左侧)和Bias评分(右侧),从上至下依次为0~6 h、6~12 h、12~18 h和18~24 h的累积降水评分,检验资料为地面气象站观测值,试验设置见表1

    Figure  9.  TS (threat score; left column) and Bias score (right column) calculated against rain gauges of surface stations for 6 h cumulated rainfall from top to bottom for 0–6 h, 6–12 h, 12–18 h, and 18–24 h forecast. Table 1 shows the experiment settings

    图  10  逐6 h检验的地面(a)2 m高度的温度、(b)10 m高度的u风、以及(c)10 m高度的v风的均方根误差结果,检验资料为地面气象站观测值,试验设置见表1

    Figure  10.  Horizontal averaged RMSE for analysis and forecast results of different experiments compared with surface observations for (a) T2m (2 m-height temperature), (b) u10m (10 m-height u), and (c) v10m (10 m-height v). Table 1 shows the experiment settings

    图  11  逐6 h检验的地面累积降水TS评分(左侧)和Bias评分(右侧),从上至下依次为0~6 h、6~12 h、12~18 h和18~24 h的累积降水评分,试验设置见表2

    Figure  11.  TS (left column) and Bias score (right column) calculated against rain gauges of surface stations for 6 h cumulated rainfall from top to bottom for 0–6, 6–12, 12–18, 18–24 h forecast. Table 2 shows the experiment settings

    图  12  2019年6月16日12:00至17日12:00(a)地面观测实况和(b–f)不同试验预报结果的24 h累积降水量(单位:mm)。试验设置见表1表2

    Figure  12.  (a) Observed and (b–f) different experiments forecast accumulated rainfall (units: mm) from 1200 UTC June 16, 2019, to 1200 UTC June 17, 2019. Tables 1 and 2 show the experiment settings

    图  13  2019年6月16日12:00 700 hPa(a)ERA5再分析资料比湿场(阴影,单位:g kg−1)和水平风场(矢量,单位:m s−1)分布以及(b–f)不同试验同化分析的比湿场与ERA5结果的差值(单位:g kg−1)分布,试验设置见表1表2

    Figure  13.  Distributions of (a) specific humidity (shaded, units: g kg−1) and horizontal wind (vectors, units: m s−1) from ERA5 reanalysis, and (b–f) the distribution of differences (units: g kg−1) between the analysis specific humidity from experiments and ERA5 result at 700 hPa at 1200 UTC June 16, 2019. Tables 1 and 2 show the experiment settings

    表  1  大尺度约束影响试验设置

    Table  1.   Large-scale constraint experiment descriptions

    试验名称运行方式是否有大尺度约束
    Pctl局部循环
    Pbld局部循环
    Fctl完全循环
    Fbld完全循环
    下载: 导出CSV

    表  2  敏感性试验设计,√表示大尺度约束中包含该变量场,×表示不包含

    Table  2.   Sensitivity experiment descriptions: “√” indicates that the large-scale constraint includes the variable field, and “×” indicates that it does not

    试验名称水平风场(uv温度场(T湿度场(μ
    Fbld
    NoUV×
    NoT×
    NoRH×
    下载: 导出CSV
  • [1] Bei N F, Zhang F Q. 2007. Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-Yu front of China [J]. Quart. J. Roy. Meteor. Soc., 133(622): 83−99. doi: 10.1002/qj.20
    [2] Benjamin S G, Weygandt S S, Brown J M, et al. 2016. A North American hourly assimilation and model forecast cycle: The rapid refresh [J]. Mon. Wea. Rev., 144(4): 1669−1694. doi: 10.1175/mwr-d-15-0242.1
    [3] Boggess A, Narcowich F J. 2009. A First Course in Wavelets with Fourier Analysis [M]. 2nd ed. Canada: Wiley, 315pp.
    [4] Dahlgren P, Gustafsson N. 2012. Assimilating host model information into a limited area model [J]. Tellus A: Dynamic Meteorology and Oceanograph, 64(1): 15836. doi: 10.3402/tellusa.v64i0.15836
    [5] Dahlgren P, Landelius T, Kållberg P, et al. 2016. A high-resolution regional reanalysis for Europe. Part 1: Three-dimensional reanalysis with the regional HIgh-Resolution Limited-Area Model (HIRLAM) [J]. Quart. J. Roy. Meteor. Soc., 142(698): 2119−2131. doi: 10.1002/qj.2807
    [6] Daley R. 1991. Atmospheric Data Analysis [M]. Cambridge: Cambridge University Press, 457pp.
    [7] Dudhia J. 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model [J]. J. Atmos. Sci., 46(20): 3077−3107. doi:10.1175/1520-0469(1989)046<3077:nsocod>2.0.co;2
    [8] Feng J, Sun J Z, Zhang Y. 2020. A dynamic blending scheme to mitigate large-scale bias in regional models [J]. J. Adv. Model. Earth Syst., 12(3): e2019MS001754. doi: 10.1029/2019ms001754
    [9] Guidard V, Fischer C. 2008. Introducing the coupling information in a limited-area variational assimilation [J]. Quart. J. Roy. Meteor. Soc., 134(632): 723−735. doi: 10.1002/qj.215
    [10] Gustafsson N, Janjić T, Schraff C, et al. 2018. Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres [J]. Quart. J. Roy. Meteor. Soc., 144(713): 1218−1256. doi: 10.1002/qj.3179
    [11] 郝民, 龚建东, 王瑞文, 等. 2015. 中国L波段探空湿度观测资料的质量评估及偏差订正 [J]. 气象学报, 73(1): 187−199. doi: 10.11676/qxxb2015.002

    Hao Min, Gong Jiandong, Wang Ruiwen, et al. 2015. The quality assessment and correction of the radiosonde humidity data biases of L-band in China [J]. Acta Meteor. Sinica (in Chinese), 73(1): 187−199. doi: 10.11676/qxxb2015.002
    [12] Herman G R, Schumacher R S. 2016. Extreme precipitation in models: An evaluation [J]. Wea. Forecasting, 31(6): 1853−1879. doi: 10.1175/waf-d-16-0093.1
    [13] Hong S Y, Pan H L. 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model [J]. Mon. Wea. Rev., 124(10): 2322−2339. doi:10.1175/1520-0493(1996)124<2322:nblvdi>2.0.co;2
    [14] Hong S Y, Lim J O J. 2006. The WRF single-moment 6-class microphysics scheme (WSM6) [J]. J. Korean Meteor. Sci., 42(2): 129−151.
    [15] Hsiao L F, Chen D S, Kuo Y H, et al. 2012. Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches [J]. Wea. Forecasting, 27(5): 1249−1263. doi: 10.1175/waf-d-11-00131.1
    [16] Hsiao L F, Huang X Y, Kuo Y H, et al. 2015. Blending of global and regional analyses with a spatial filter: Application to typhoon prediction over the western North Pacific Ocean [J]. Wea. Forecasting, 30(3): 754−770. doi: 10.1175/waf-d-14-00047.1
    [17] 黄丽萍, 陈德辉, 邓莲堂, 等. 2017. GRAPES_Meso V4.0主要技术改进和预报效果检验 [J]. 应用气象学报, 28(1): 25−37. doi: 10.11898/1001-7313.20170103

    Huang Liping, Chen Dehui, Deng Liantang, et al. 2017. Main technical improvements of GRAPES_Meso V4.0 and verification [J]. J. Appl. Meteor. Sci. (in Chinese), 28(1): 25−37. doi: 10.11898/1001-7313.20170103
    [18] Kalnay E. 2003. Atmospheric Modeling, Data Assimilation and Predictability [M]. Cambridge: Cambridge University Press, 341pp.
    [19] Kleist D T, Ide K. 2015a. An osse-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants [J]. Mon. Wea. Rev., 143(2): 452−470. doi: 10.1175/mwr-d-13-00350.1
    [20] Kleist D T, Ide K. 2015b. An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results [J]. Mon. Weat. Rev., 143(2): 433−451. doi: 10.1175/mwr-d-13-00351.1
    [21] 刘晶, 周玉淑, 杨莲梅, 等. 2019. 伊犁河谷一次极端强降水事件水汽特征分析 [J]. 大气科学, 43(5): 959−974. doi: 10.3878/j.issn.1006-9895.1901.18114

    Liu Jing, Zhou Yushu, Yang Lianmei, et al. 2019. A diagnostic study of water vapor during an extreme precipitation event in the Yili River valley [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(5): 959−974. doi: 10.3878/j.issn.1006-9895.1901.18114
    [22] Lock A P, Brown A R, Bush M R, et al. 2000. A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests [J]. Mon. Wea. Rev., 128(9): 3187−3199. doi:10.1175/1520-0493(2000)128<3187:anblms>2.0.co;2
    [23] Lorenc A C. 1986. Analysis methods for numerical weather prediction [J]. Quart. J. Roy. Meteor. Soc., 112(474): 1177−1194. doi: 10.1002/qj.49711247414
    [24] 马旭林, 庄照荣, 薛纪善, 等. 2009. GRAPES非静力数值预报模式的三维变分资料同化系统的发展 [J]. 气象学报, 67(1): 50−60. doi: 10.11676/qxxb2009.006

    Ma Xulin, Zhuang Zhaorong, Xue Jishan, et al. 2009. Development of 3-D variational data assimilation system for the nonhydrostatic numerical weather prediction model-GRAPES [J]. Acta Meteor. Sinica (in Chinese), 67(1): 50−60. doi: 10.11676/qxxb2009.006
    [25] Milbrandt J A, Bélair S, Faucher M, et al. 2016. The Pan-Canadian high resolution (2.5 km) deterministic prediction system [J]. Wea. Forecasting, 31(6): 1791−1816. doi: 10.1175/waf-d-16-0035.1
    [26] Mlawer E J, Taubman S J, Brown P D, et al. 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave [J]. J. Geophys. Res., 102(D14): 16663−16682. doi: 10.1029/97jd00237
    [27] Pielke R A. 2013. Mesoscale Meteorological Modeling [M]. 3rd ed. Amsterdam: Elsevier, 760pp.
    [28] Schlüter I, Schädler G. 2010. Sensitivity of heavy precipitation forecasts to small modifications of large-scale weather patterns for the Elbe River [J]. J. Hydrometeorol., 11(3): 770−780. doi: 10.1175/2010jhm1186.1
    [29] Schraff C, Reich H, Rhodin A, et al. 2016. Kilometre-scale ensemble data assimilation for the COSMO model (KENDA) [J]. Quart. J. Roy. Meteor. Soc., 142(696): 1453−1472. doi: 10.1002/qj.2748
    [30] Seo B C, Quintero F, Krajewski W F. 2018. High-resolution QPF uncertainty and its implications for flood prediction: A case study for the Eastern Iowa flood of 2016 [J]. J. Hydrometeorol., 19(8): 1289−1304. doi: 10.1175/jhm-d-18-0046.1
    [31] Skamarock W C. 2004. Evaluating mesoscale NWP models using kinetic energy spectra [J]. Mon. Wea. Rev., 132(12): 3019−3032. doi: 10.1175/mwr2830.1
    [32] Sun J Z, Xue M, Wilson J W, et al. 2014. Use of NWP for nowcasting convective precipitation: Recent progress and challenges [J]. Bull. Amer. Meteor. Soc., 95(3): 409−426. doi: 10.1175/bams-d-11-00263.1
    [33] Tong W X, Li G, Sun J Z, et al. 2016. Design strategies of an hourly update 3DVAR data assimilation system for improved convective forecasting [J]. Wea. Forecasting, 31(5): 1673−1695. doi: 10.1175/waf-d-16-0041.1
    [34] Vendrasco E P, Sun J Z, Herdies D L, et al. 2016. Constraining a 3DVAR radar data assimilation system with large-scale analysis to improve short-range precipitation forecasts [J]. J. Appl. Meteor. Climatol., 55(3): 673−690. doi: 10.1175/jamc-d-15-0010.1
    [35] 徐枝芳, 郝民, 朱立娟, 等. 2013. GRAPES_RAFS系统研发 [J]. 气象, 39(4): 466−477. doi: 10.7519/j.issn.1000-0526.2013.04.009

    Xu Zhifang, Hao Min, Zhu Lijuan, et al. 2013. On the research and development of GRAPES_RAFS [J]. Meteor. Mon. (in Chinese), 39(4): 466−477. doi: 10.7519/j.issn.1000-0526.2013.04.009
    [36] 薛纪善, 庄世宇, 朱国富, 等. 2008. GRAPES新一代全球/区域变分同化系统研究 [J]. 科学通报, 53(22): 3346−3457. doi: 10.1360/csb2008-53-20-2408

    Xue Jishan, Zhuang Shiyu, Zhu Guofu, et al. 2008. Scientific design and preliminary results of three- dimensional variational data assimilation system of GRAPES [J]. Chinese Sci. Bull., 53(22): 3346−3457. doi: 10.1360/csb2008-53-20-2408
    [37] Yang L, Smith J. 2018. Sensitivity of extreme rainfall to atmospheric moisture content in the arid/semiarid southwestern United States: Implications for probable maximum precipitation estimates [J]. J. Geophys. Res., 123(3): 1638−1656. doi: 10.1002/2017jd027850
    [38] Yang M J, Gong J D, Wang R C, et al. 2019. A comparison of the blending and constraining methods to introduce large-scale information into GRAPES mesoscale analysis [J]. J. Trop. Meteor., 25(2): 227−244. doi: 10.16555/j.1006-8775.2019.02.009
    [39] Yano J I, Ziemiański M Z, Cullen M, et al. 2018. Scientific challenges of convective-scale numerical weather prediction [J]. Bull. Amer. Meteor. Soc., 99(4): 699−710. doi: 10.1175/bams-d-17-0125.1
    [40] Yue X J, Shao A M, Fang X, et al. 2018. Incorporating a large-scale constraint into radar data assimilation to mitigate the effects of large-scale bias on the analysis and forecast of a squall line over the Yangtze–Huaihe River basin [J]. J. Geophys. Res., 123(16): 8581−8598. doi: 10.1029/2018jd028362
    [41] 曾智琳, 谌芸, 朱克云. 2019. 2017年6月一次华南沿海强降水的对流性特征及热动力机制研究 [J]. 大气科学, 43(6): 1295−1312. doi: 10.3878/j.issn.1006-9895.1901.18207

    Zeng Zhilin, Chen Yun, Zhu Keyun. 2019. Convective characteristics and thermal dynamic mechanisms for coastal torrential rainfall over South China during June 2017 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(6): 1295−1312. doi: 10.3878/j.issn.1006-9895.1901.18207
    [42] 张华, 薛纪善, 庄世宇, 等. 2004. GRAPeS三维变分同化系统的理想试验 [J]. 气象学报, 62(1): 31−41. doi: 10.11676/qxxb2004.004

    Zhang Hua, Xue Jishan, Zhuang Shiyu, et al. 2004. Idea experiments of GRAPeS three-dimensional variational data assimilation system [J]. Acta Meteor. Sinica (in Chinese), 62(1): 31−41. doi: 10.11676/qxxb2004.004
    [43] 张景, 周玉淑, 沈新勇, 等. 2019. 2016年“7.19”京津冀极端降水系统的动热力结构及不稳定条件分析 [J]. 大气科学, 43(4): 930−942. doi: 10.3878/j.issn.1006-9895.1812.18231

    Zhang Jing, Zhou Yushu, Shen Xinyong, et al. 2019. Evolution of dynamic and thermal structure and instability condition analysis of the extreme precipitation system in Beijing–Tianjin–Hebei on July 19 2016 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(4): 930−942. doi: 10.3878/j.issn.1006-9895.1812.18231
    [44] Zhang L, Liu Y Z, Liu Y, et al. 2019. The operational global four‐dimensional variational data assimilation system at the China Meteorological Administration [J]. Quart. J. Roy. Meteor. Soc., 145(722): 1882−1896. doi: 10.1002/qj.3533
    [45] 张文龙, 崔晓鹏, 黄荣, 等. 2019. 北京“623”大暴雨的强降水超级单体特征和成因研究 [J]. 大气科学, 43(5): 1171−1190. doi: 10.3878/j.issn.1006-9895.1905.18230

    Zhang Wenlong, Cui Xiaopeng, Huang Rong, et al. 2019. An investigation of the characteristics and mechanism of the high precipitation supercell in the Beijing “623” severe rainstorm [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(5): 1171−1190. doi: 10.3878/j.issn.1006-9895.1905.18230
    [46] 郑永骏, 金之雁, 陈德辉. 2008. 半隐式半拉格朗日动力框架的动能谱分析 [J]. 气象学报, 66(2): 143−157. doi: 10.11676/qxxb2008.015

    Zheng Yongjun, Jin Zhiyan, Chen Dehui. 2008. Kinetic energy spectrum analysis in a semi implicit semi Lagrangian dynamical framework [J]. Acta Meteor. Sinica (in Chinese), 66(2): 143−157. doi: 10.11676/qxxb2008.015
    [47] 庄世宇, 薛纪善, 朱国富, 等. 2005. GRAPES全球三维变分同化系统——基本设计方案与理想试验 [J]. 大气科学, 29(6): 872−884. doi: 10.3878/j.issn.1006-9895.2005.06.04

    Zhuang Shiyu, Xue Jishan, Zhu Guofu, et al. 2005. GRAPES global 3D-Var system—Basic scheme design and single observation test [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 29(6): 872−884. doi: 10.3878/j.issn.1006-9895.2005.06.04
    [48] 庄照荣, 陈静, 黄丽萍, 等. 2018. 全球和区域分析的混合方案对区域预报的影响试验 [J]. 气象, 44(12): 1509−1517. doi: 10.7519/j.issn.1000-0526.2018.12.001

    Zhuang Zhaorong, Chen Jing, Huang Liping, et al. 2018. Impact experiments for regional forecast using blending method of global and regional analyses [J]. Meteor. Mon. (in Chinese), 44(12): 1509−1517. doi: 10.7519/j.issn.1000-0526.2018.12.001
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出版历程
  • 收稿日期:  2020-07-06
  • 录用日期:  2021-04-08
  • 网络出版日期:  2021-06-03

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