GNSS/MET Water Vapor Data: Quality Control Method and Comparative Analysis of Reanalysis Datasets
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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。
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关键词:
- 地基GNSS/MET水汽 /
- 质量控制 /
- 中国大气再分析资料 /
- 对比评估
Abstract: This study proposes a comprehensive quality control (CQC) algorithm for the Chinese ground-based navigation satellite system (GNSS) water-vapor products. The CQC algorithm consists of two sections—Quality check and comprehensive decision-making algorithms. The quality check algorithm consists of seven parts, namely, limit check to eliminate errors that exceed reasonable limits, buddy and low-pass filter checks for better time consistency, neighboring station check, anomaly and peak–valley value checks for better spatial consistency, and background check to identify the data that deviate from the background field for assimilation application. After each check, the data that exceed the threshold are identified, and the comprehensive decision-making algorithm is used to score the identified data and flag (correct, suspicious, or error) the data. Based on the quality-controlled observation data, the precipitable water-vapor simulation of five sets of reanalysis data, including China’s first-generation global atmosphere reanalysis (CRA) product, was evaluated. The results show that the simulated total water vapor of all the reanalysis data in winter is slightly higher than the observation in winter and significantly lower than the observation in summer. Spatially, the simulated water vapor content in southern and western China is lower than the observation, and this situation is more obvious in the summer half of the year. Relative to the observation, the average bias (O−B) of CRA is −0.633 mm, and the root–mean–square error is 3.650 mm. The deviation of CRA relative to the observation is slightly lower than ERA-Interim but slightly higher than ERA5, which is significantly better than JRA55 and NCEP2. -
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 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 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
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 PS10 PS5 PS1 PS0.1 临近点检查PT ZTD 14.0 18.0 27.5 37.0 PWV 2.0 3.0 5.0 8.0 滤波检查PF ZTD 10.0 13.0 20.5 31.5 PWV 1.5 2.0 3.5 6.0 邻近站检查PN ZTD 43.0 56.0 86.0 134.0 PWV 4.5 6.0 10.0 17.5 距平检查PA ZTD 68.0 82.0 114.0 220.0 PWV 11.0 14.0 19.0 28.0 背景场检查（PWV，PB） OMB<0 −4.5 −5.5 −9.0 −23.5 OMB>0 7.0 9.0 14.0 23.0 
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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
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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。
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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
2018 2019 质量控制前 3.726 3.678 质量控制后 3.650 3.650
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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
2018 ERA-Int CRA ERA5 Bias 0.762 0.633 0.542 RMSE 3.789 3.650 3.511
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