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Quality Control Method for Land Surface Hourly Precipitation Data in China
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摘要: 高时空分辨率自动气象站降水观测作为重要数据来源, 已被广泛应用于强对流天气监测、模式评估、预报复盘等研究工作。仪器故障、特殊天气条件下观测设备的局限性等因素是自动气象站降水数据不确定性的主要来源, 这些问题在无人值守气象站尤为突出。该研究基于2021—2023年中国自动气象站实时观测降水量数据、高时空分辨率雷达数据和高灵敏性降水类天气现象数据, 发展适应于中国自动气象站小时降水数据的多源数据协同质量控制方法(multi-source data collaborative quality control, MDC)。通过综合定量指标与典型个例分析, 对MDC的应用效果进行全面评估。结果显示:MDC判识正确率为99.92%, 错误数据命中率较现行业务提升39.3%。基于多源降水观测数据时空一致性, MDC显著提升了晴空降水、融雪性降水和虚假零值降水等异常数据的甄别能力, 有效弥补了传统方法的不足。Abstract: High spatial-temporal resolution observations of precipitation from automatic weather stations (AWSs)serve as a vital data source, extensively utilized in research activities such as severe weather monitoring, model evaluation, and forecast analysis. Influenced by factors such as observation environments and equipment performance, precipitation observations inevitably contain various forms of random and systematic errors. A quality control method (multi-source data collaborative quality control, MDC) has been established for hourly precipitation data from AWSs in China, based on high spatial-temporal resolution radar data and weather phenomena. The MDC includes three modules: Precipitation self-detection, multi-source data collaborative detection, and dynamic blacklisting. The MDC has been applied to quality control of hourly precipitation data from AWSs from 2021 to 2023. A comprehensive effectiveness assessment of the method has been conducted using a combination of quantitative indicators and case analyses of detection effects on various types of erroneous data. Results indicate that the correct identification rate of the MDC reaches 99.92%, with a false exclusion rate of 0.08%. The majority of falsely excluded data consists of weak precipitation amounts ranging from 0.1-1 mm, accounting for 60.72%. While ensuring a high correct rate, the MDC also demonstrates a high capability in identifying erroneous data. The average error data hit rate of the MDC in China is 39.8%, which represents an improvement of 39.3% over the existing Meteorological Ddata Operatioin System (MDOS) real-time quality control system. The ability MDC to identify erroneous data between 0-50 mm is approximately 40%, and this hit rate significantly increases with higher precipitation values. When precipitation amounts exceed 100 mm, the hit rate achieves 100%. MDOS real-time quality control system has an almost zero hit rate for erroneous data with precipitation amounts less than 20 mm but possesses some identification capability for abnormal precipitation of more than 20 mm.The hit rate of the MDC shows significant spatial variation due to the coverage of radar and national station observations. In the eastern region, where observation stations are densely distributed, most stations have an error data hit rate of over 90%. However, in the western and northeastern regions, where observation stations are sparse and do not meet the conditions for multi-source collaborative detection, the hit rate of the MDC decreases significantly, approaching that of the MDOS real-time quality control. Case analyses of the quality control effects on different types of erroneous data reveal that the MDC significantly the identification ability of abnormal data such as clear sky precipitation, snowmelt precipitation, and false zero value precipitation, effectively making up for the deficiencies of traditional methods.
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图 1 2021—2023年误剔数据降水量频率分布
Fig. 1 Frequency distribution of incorrectly excluded data for precipitation values from 2021 to 2023
图 2 2022—2023年更正报更正前后降水量频率分布
Fig. 2 Frequency of precipitation before and after correction report from 2022 to 2023
图 3 2022—2023年MDOS和MDC不同降水强度错误数据命中率
Fig. 3 Hit rate of erroneous data for different precipitation intensities by MDOS and MDC from 2022 to 2023
图 4 2022—2023年中国地区错误数据命中率空间分布
Fig. 4 Spatial distributions of error data hit rate from 2022 to 2023
图 5 2021年3月22日00:00—23:00吉林河南屯站1 h降水量(实线) 及台站界限值检查的最大阈值(虚线)
Fig. 5 Hourly precipitation (the solid line) at Henantun Station in Jilin from 0000 UTC to 2300 UTC on 22 Mar 2021 and the maximum threshold (the dashed line) for climatic checks
图 6 2021年3月22日04：00质量控制前后1 h降水量空间分布
Fig. 6 Spatial distribution of hourly precipitation before and after quality control at 0400 UTC 22 Mar 2021
图 7 2023年7月28日02:00成都大运会赛区质量控制前、后1 h降水量空间分布
Fig. 7 Spatial distribution of hourly precipitation before and after quality control in Chengdu Universiade Competition Area at 0200 UTC 28 Jul 2023
图 8 2022年2月5日00：00—10日23：00华中地区发生液态降水和固态降水的国家站数量
Fig. 8 Number of national stations in the Central China with liquid precipitation and solid precipitation from 0000 UTC 5 Feb to 2300 UTC 9 Feb in 2022
图 9 2022年2月9日06:00华中地区国家站和区域站质量控制前后小时降水量
Fig. 9 Hourly precipitation at national stations, regional stations before and after quality control at 0600 UTC 9 Feb 2022
图 10 2023年7月31日04：00质量控制前后北京西部自动气象站1 h降水量空间分布
Fig. 10 Spatial distribution of hourly precipitation at automatic stations before and after quality control in the west of Beijing at 0400 UTC 31 Jul 2023
图 11 2023年4月2—4日自动气象站和国家站72 h累积降水量空间分布
Fig. 11 Spatial distribution of 72 h accumulated precipitation at automatic weather stations and national stations from 2 Apr to 4 Apr in 2023
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