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中国地基GNSS/MET水汽产品质量控制及与再分析产品的对比评估

远芳 廖捷 周自江

远芳, 廖捷, 周自江. 2022. 中国地基GNSS/MET水汽产品质量控制及与再分析产品的对比评估[J]. 大气科学, 46(5): 1132−1146 doi: 10.3878/j.issn.1006-9895.2110.21139
引用本文: 远芳, 廖捷, 周自江. 2022. 中国地基GNSS/MET水汽产品质量控制及与再分析产品的对比评估[J]. 大气科学, 46(5): 1132−1146 doi: 10.3878/j.issn.1006-9895.2110.21139
YUAN Fang, LIAO Jie, ZHOU Zijiang. 2022. GNSS/MET Water Vapor Data: Quality Control Method and Comparative Analysis of Reanalysis Datasets [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(5): 1132−1146 doi: 10.3878/j.issn.1006-9895.2110.21139
Citation: YUAN Fang, LIAO Jie, ZHOU Zijiang. 2022. GNSS/MET Water Vapor Data: Quality Control Method and Comparative Analysis of Reanalysis Datasets [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(5): 1132−1146 doi: 10.3878/j.issn.1006-9895.2110.21139

中国地基GNSS/MET水汽产品质量控制及与再分析产品的对比评估

doi: 10.3878/j.issn.1006-9895.2110.21139
基金项目: 国家气象信息中心结余资金项目 NMICJY202105,国家自然科学基金重大项目42090033,国家重点研究发展计划2017YFC1501801
详细信息
    作者简介:

    远芳,女,1984年出生,博士,主要从事气象观测资料质量控制和数据产品研发。E-mail: yuanfang@cma.gov.cn

    通讯作者:

    E-mail:liaoj@cma.gov.cn

  • 中图分类号: P426

GNSS/MET Water Vapor Data: Quality Control Method and Comparative Analysis of Reanalysis Datasets

Funds: National Meteorological Information Centre Surplus Funds Program (Grant NMICJY202105), National Natural Science Foundation of China (Grant 42090033), National Key R&D Program of China (Grant 2017YFC1501801)
  • 摘要: 本文研究并提出中国地基全球导航卫星系统(GNSS)水汽产品的综合质量控制(CQC)算法。CQC算法由质量检查和综合决策两个环节组成。质量检查环节主要对待检观测数据与其参考数据的差异进行分析,包括界限值检查、考察时间一致性的临近点检查和低通滤波检查,考察空间一致性的邻近站检查、距平值检查和峰谷值检查,以及基于背景场的粗大误差检查等7个模块。每个检查标记出超过阈值的观测数据,随后利用综合决策算法对数据的标记情况进行综合评分,最终给出数据的质量控制码。基于质量控制后的数据,评估了中国第一代全球大气再分析产品(CRA)、ERA-Interim和ERA5等五套再分析数据在中国地区的水汽模拟效果。结果表明几套再分析资料模拟的大气可降水量(PWV)在冬季整体略高于观测,夏季则明显低于观测。在空间上,中国南方地区和西部地区模拟的PWV低于观测,这种情况在夏半年更加明显。相对于观测,CRA的平均偏差(O−B)为0.633 mm,均方根误差为3.650 mm。CRA相对于观测的误差略高于ERA5,但略低于ERA-Interim,明显优于JRA55和NCEP2结果。
  • 图  1  地基全球卫星导航系统气象GNSS/MET台站分布,红点代表气象局(CMA)观测站点,蓝点代表中国大陆构造环境监测网络CMONOC观测站点

    Figure  1.  Distribution of GNSS/MET (Ground-based Navigation Satellite System/METeorology) sites. The red dots denote the CMA (China Meteorological Administration) sites, whereas the blue dots denote the CMONOC (Crustal Movement Observation Network Of China) sites

    图  2  GNSS/MET水汽产品质量控制流程

    Figure  2.  Quality control flow of the GNSS/MET data

    图  3  山西汾阳站(红色十字,站号53769)及其周围站点,其中红点为所选的汾阳站的邻近站

    Figure  3.  Fengyang station of Shanxi Province (red cross, station ID: 53769) and nearby stations. The red dots denote the selected reference stations

    图  4  山西永和站(站号53852)2019年9月9日12:00(图中横坐标0时刻)及前后24 h大气可降水量PWV变化(黑色实线)及相关QC阈值(PS5参数。粉线:临近点检查,蓝线:滤波检查,橙线:邻近站检查,紫线:距平值检查,绿点:背景场检查,深红线:峰/谷值检查)。灰色区域是能够PS5参数条件下通过CQC的数据取值范围

    Figure  4.  GNSS/MET precipitable water vapor PWV data (black dotted line) 24 h before and after 1200 UTC on September 9, 2019, from Yonghe station in Shanxi Province (station ID 53852) and the associated QC thresholds of buddy check (pink line), low-pass filter check (blue line), neighboring station check (orange line), anomaly check (purple line), background check (green dots), peak–valley value check (carmine line) (PS5 parameters; the gray shaded area denotes the range of the data that can pass CQC under the PS5 parameter conditions)

    图  5  不同错误类型所占比例

    Figure  5.  Proportions of different error types

    图  6  同图4,但为(a)湖北宜昌(站号57461)2019年8月27日12:00,(b)河北滦平(站号54420)2019年7月13日12:00,(c)重庆巫山(站号57349)2019年7月3日05:00,(d)江苏徐州(站号58027)2019年7月15日06:00 PWV观测和PS1条件下的阈值范围

    Figure  6.  Same as Fig.4, but for (a) Yichang station in Hubei Province (station ID 57461), (b) Luanping station in Hebei Province (station ID 54420), (c) Wushan station in Chongqing Province (station ID 57349), and (d) Xuzhou station in Jiangsu Province (station ID 58027). The gray shaded area denotes the range of the data that can pass CQC under the PS1 parameter conditions

    图  7  2018年全国台站ZTD质量控制前(蓝线)后(红线)不同百分位值的分布

    Figure  7.  Time series of the ZTD percentiles of all sites before (blue line) and after (red line) quality control in 2018

    图  8  (a)2019年4月1~7日18:00质量控制前(红点加蓝点)、后(蓝点)PWV观测值(Obs)与背景场(CRA)对比的散点图;(b)2018年未通过综合质量控制的观测场(Obs)与背景场(CRA)的散点图。图中黑色虚线是观测与背景场的线性拟合结果,灰色虚线是若观测值与背景场相等时线性拟合结果。

    Figure  8.  Comparison of PWV between observation and background field (CRA): (a) The red dots denote the error data detected by the QC algorithm at 1800 UTC from April 1 to 7, 2019; (b) blue dots denote the error data detected by the QC algorithm in 2018 (the black dashed line denotes the linear fitting, the gray dashed line denotes the linear fitting when the observations equal to the background data)

    图  9  2018~2019年每日00:00质量控制前(黑线)后(红线)用于与背景场进行比较的(a)PWV的数据量和(b)观测与背景场的均方根误差(RMSE)时间序列

    Figure  9.  Data quality of (a) CRA for PWV and (b) RMSE of observation and background fields at 0000 UTC everyday of 2018–2019:Before (black) and after (red) quality control

    图  10  2018~2019年00:00 GNSS/MET PWV观测与几套再分析(CRA(红线)、ERA-Interim(蓝线)、ERA5(黑线)、JRA55(黄线)和NCEP2(绿线))数据的偏差(Bias, a)和均方根误差(RMSE, b)时间序列

    Figure  10.  Time series of (a) Bias and (b) RMSE of PWV observation and reanalysis: CRA (red), ERA-Interim (blue), ERA5 (black), JRA55 (yellow) and NCEP2 (green) at 0000 UTC of 2018–2019

    图  11  2018年1~3月每日00:00 GNSS/MET PWV观测数据分别与CRA、ERA5再分析数据的对比分析:(a)PWV与CRA的偏差(BiasC);(b)PWV与ERA5的偏差(BiasE);(c)PWV与CRA的均方根误差(RMSEC);(d)PWV与ERA5的均方根误差(RMSEE);(e)BiasC与BiasE的差值;(f)RMSEC与RMSEE的差值

    Figure  11.  Comparison of observation and reanalysis data: (a) Bias between PWV observation and CRA (BiasC); (b) Bias of PWV observation and ERA5 (BiasE); (c) RMSE of PWV observation and CRA (RMSEC); (d) RMSE between PWV observation and ERA5 (RMSEE); (e) difference between BiasC and BiasE; (f) difference between RMSEC and RMSEE for January to March at 0000 UTC everyday of 2018

    图  12  图11,但为2018年7~9月每日00:00数据

    Figure  12.  As in Fig.11, but for July, August and September at 0000 UTC everyday of 2018

    表  1  不同标记比例下的参数

    Table  1.   Parameters of different flag rate

    检查模块要素参数/mm
    PS10PS5PS1PS0.1
    临近点检查PTZTD14.018.027.537.0
    PWV2.03.05.08.0
    滤波检查PFZTD10.013.020.531.5
    PWV1.52.03.56.0
    邻近站检查PNZTD43.056.086.0134.0
    PWV4.56.010.017.5
    距平检查PAZTD68.082.0114.0220.0
    PWV11.014.019.028.0
    背景场检查(PWV,PBOMB<0−4.5−5.5−9.0−23.5
    OMB>07.09.014.023.0
    下载: 导出CSV

    表  2  各检查模块权重

    Table  2.   Weights of the checks

    检查权重
    界限值检查2.0
    临近点检查(T)0.555
    低通滤波检查(F)0.65
    邻近站检查(N)0.60
    距平值检查(A)0.59
    峰/谷值检查(P)1.53
    基于背景场粗大误差检(B)0.69
    下载: 导出CSV

    表  3  最终判断算法

    Table  3.   Decision making algorithm

    质控码判断条件
    0
    (正确)
    (1)数据通过了全部检查;
    (2)数据仅未通过其中任何1项检查,且SQ小于1.0,则认为该数据基本正确;
    1
    (可疑)
    (3)某数据未通过其中2项检查,且这2项都是针对时间序列的检查(临近点检查和滤波检查),且SQ小于1.5;
    (4)某数据未通过其中2项检查,且这2项都是针对空间场的检查(距平检查和邻近站检查),且SQ小于1.5;
    2
    (错误)
    (5)若某数据未通过2项检查,其中1项针对时间序列的检查(临近点检查或滤波检查)同时另1项针对空间场的检查(距平检查或邻近站检查);
    (6)SQ大于等于1.5。
    下载: 导出CSV

    表  4  2018~2019年每日00:00质量控制前后PWV观测与CRA的均方根误差(RMSE)

    Table  4.   RMSE of observation and CRA for PWV before and after quality control at 0000 UTC everyday of 2018–2019

    20182019
    质量控制前3.7263.678
    质量控制后3.6503.650
    下载: 导出CSV

    表  5  2018年GNSS/MET PWV 00:00观测资料与CRA、ERA-Interim和ERA5再分析资料的平均偏差(Bias)和均方根误差(RMSE)

    Table  5.   Averaged Bias and RMSE of PWV observation and reanalysis of CRA, ERA-Interim and ERA5 for the year 2018 at 0000 UTC

    2018ERA-IntCRAERA5
    Bias0.7620.6330.542
    RMSE3.7893.6503.511
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-01
  • 录用日期:  2021-12-21
  • 网络出版日期:  2022-01-05
  • 刊出日期:  2022-09-22

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