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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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[1] 林佳璐, 李英, 柳龙生.风暴-低涡影响下青藏高原一次强降水过程.应用气象学报, 2023, 34(2):166-178. doi: 10.11898/1001-7313.20230204Lin J L, Li Y, Liu L S. A heavy precipitation process over the Tibetan Plateau under the joint effects of a tropical cyclone and vortex. J Appl Meteor Sci, 2023, 34(2): 166-178. doi: 10.11898/1001-7313.20230204 [2] 王国荣, 卞素芬, 王令, 等. 用地面加密自动观测资料对北京地区一次飑线过程的分析. 气象, 2010, 36(6): 59-65.Wang G R, Bian S F, Wang L, et al. Analysis on a typical squall line case with surface automatic weather observations. Meteor Mon, 2010, 36(6): 59-65. [3] 邢楠, 仲跻芹, 雷蕾, 等. 基于CMA-BJ的北京地区短时强降水预报试验. 应用气象学报, 2023, 34(6): 641-654. doi: 10.11898/1001-7313.20230601Xing N, Zhong J Q, Lei L, et al. A probabilistic forecast experiment of short-duration heavy rainfall in Beijing based on CMA-BJ. J Appl Meteor Sci, 2023, 34(6): 641-654. doi: 10.11898/1001-7313.20230601 [4] 东高红, 刘黎平. 雷达与雨量计联合估测降水的相关性分析. 应用气象学报, 2012, 23(1): 30-39. http://qikan.camscma.cn/article/id/20120104Dong G H, Liu L P. Correlation analysis on estimating rainfall using radar-rain gauge calibration. J Appl Meteor Sci, 2012, 23(1): 30-39. http://qikan.camscma.cn/article/id/20120104 [5] Zhang J, Howard K, Langston C, et al. National mosaic and multi-sensor QPE(NMQ) system: Description, results and future plans. Bull Amer Meteor Soc, 2011, 92: 1321-1338. doi: 10.1175/2011BAMS-D-11-00047.1 [6] 刘菲凡, 郑永光, 罗琪, 等. 京津冀及周边一般性降水与短时强降水特征对比. 应用气象学报, 2023, 34(5): 619-629. doi: 10.11898/1001-7313.20230510Liu F F, Zheng Y G, Luo Q, et al. Comparison of characteristics of light precipitation and short-time heavy precipitation over Beijing, Tianjin, Hebei and neighbouring areas. J Appl Meteor Sci, 2023, 34(5): 619-629. doi: 10.11898/1001-7313.20230510 [7] 任芝花, 冯明农, 张洪政, 等. 自动与人工观测降雨量的差异及相关性. 应用气象学报, 2007, 18(3): 358-364. doi: 10.3969/j.issn.1001-7313.2007.03.012Ren Z H, Feng M N, Zhang H Z, et al. The difference and relativity between rainfall by automatic recording and manual observation. J Appl Meteor Sci, 2007, 18(3): 358-364. doi: 10.3969/j.issn.1001-7313.2007.03.012 [8] 宝兴华, 夏茹娣, 罗亚丽, 等. "21·7" 河南特大暴雨气象和水文雨量观测对比. 应用气象学报, 2022, 33(6): 668-681. doi: 10.11898/1001-7313.20220603Bao X H, Xia R D, Luo Y L, et al. Comparative analysis on meteorological and hydrological rain gauge observations of the extreme heavy rainfall event in Henan Province during July 2021. J Appl Meteor Sci, 2022, 33(6): 668-681. doi: 10.11898/1001-7313.20220603 [9] 齐道日娜, 何立富, 王秀明, 等. "7·20" 河南极端暴雨精细观测及热动力成因. 应用气象学报, 2022, 33(1): 1-15.Chyi D, He L F, Wang X M, et al. Fine observation characteristics and thermodynamic mechanisms of extreme heavy rainfall in Henan on 20 July 2021. J Appl Meteor Sci, 2022, 33(1): 1-15. [10] 常国旭, 董秀辉, 刘红艳, 等. 加密自动气象站实时短信报警查询系统. 安徽农业科学, 2009, 37(1): 427-428.Chang G X, Dong X H, Liu H Y, et al. Real-time short message alarming and information query system for intensive automatic weather station network. J Anhui Agric Sci, 2009, 37(1): 427-428. [11] Martinaitis S M. Effects of Multi-sensor Radar and Rain Gauge Data on Hydrologic Modeling in Relatively Flat Terrain. Florida: Florida State University, 2008. [12] Sevruk B. Rainfall Measurement: Gauges. Encyclopedia of Hydrological Sciences. Anderson M G, Ed. 2005. DOI: 10.1002/047084-8944.hsa038 .[13] Habib E, Krajewski W F, Kruger A. Sampling errors of tipping-bucket rain gauge measurements. J Hydrol Eng, 2001, 6(2): 159-166. [14] Kondragunta C R, Shrestha K. Automated Real-time Operational Rain Gauge Quality-control Tools in NWS Hydrologicoperations. 20th Conf on Hydrology, Boston, MA, Amer Meteor Soc, 2006: P2.4. [15] Sieck L C, Burges S J, Steiner M. Challenges in obtaining reliable measurements of point rainfall. Water Resour Res, 2007, 43(1). DOI: 10.1029/2005WR004519. [16] 叶柏生, 杨大庆, 丁永建, 等. 中国降水观测误差分析及其修正. 地理学报, 2007, 62(1): 3-13.Ye B S, Yang D Q, Ding Y J, et al. A bias-corrected precipitation climatology for China. Acta Geographica Sinica, 2007, 62(1): 3-13. [17] Goodison B E, Louie P Y T, Yang D. WMO Solid Precipitation Measurement Intercomparison. WMO Instruments and Observing Methods Rep No 67, 1998, WMO/TD-No. 872. [18] Rasmussen R, Baker B, Kochendorfer J, et al. How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull Amer Meteor Soc, 2012, 93(6): 811-829. [19] Nitu R, and Coauthors. WMO Solid Precipitation Intercomparison Experiment(SPICE)(2012-2015). IOM Rep 131, 2008. [20] 陶士伟, 徐枝芳. 加密自动站资料质量保障体系分析. 气象, 2007, 33(2): 34-41.Tao S W, Xu Z F. Analysis of the quality assurance procedures in intensified automatic surface weather observation system. Meteor Mon, 2007, 33(2): 34-41. [21] 杨萍, 刘伟东, 仲跻芹, 等. 北京地区自动气象站气温观测资料的质量评估. 应用气象学报, 2011, 22(6): 706-715.Yang P, Liu W D, Zhong J Q, et al. Evaluating the quality of temperature measured at automatic weather stations in Beijing. J Appl Meteor Sci, 2011, 22(6): 706-715. [22] Fiebrich C A, Crawford K C. The impact of unique meteorological phenomena detected by the Oklahoma mesonet and ARS micronet on automated quality control. Bull Amer Meteor Soc, 2001, 82(10): 2173-2187. [23] 任芝花, 赵平, 张强, 等. 适用于全国自动站小时降水资料的质量控制方法. 气象, 2010, 36(7): 123-132.Ren Z H, Zhao P, Zhang Q, et al. Quality control procedures for hourly precipitation data from automatic weather stations in China. Meteor Mon, 2010, 36(7): 123-132. [24] 任芝花, 张志富, 孙超, 等. 全国自动气象站实时观测资料三级质量控制系统研制. 气象, 2015, 41(10): 1268-1277.Ren Z H, Zhang Z F, Sun C, et al. Development of three-step quality control system of real-time observation data from AWS in China. Meteor Mon, 2015, 41(10): 1268-1277. [25] Kim D, Nelson B, Seo D J. Characteristics of reprocessed hydrometeorological automated data system(HADS) hourly precipitation data. Wea Forecasting, 2009, 24(5): 1287-1296. [26] 陶士伟, 仲跻芹, 徐枝芳, 等. 地面自动站资料质量控制方案及应用. 高原气象, 2009, 28(5): 1202-1209.Tao S W, Zhong J Q, Xu Z F, et al. Quality control schemes and its application to automatic surface weather observation system. Plateau Meteor, 2009, 28(5): 1202-1209. [27] 张乐坚, 俞小鼎, 李峰, 等. 地面降水的多源数据辅助质量控制方法. 气象, 2016, 42(3): 363-371.Zhang L J, Yu X D, Li F, et al. Quality control method for multi-source data of surface rainfall. Meteor Mon, 2016, 42(3): 363-371. [28] 张志强, 仲凌志, 杨和平. 天气雷达在中国自动气象站实时质量控制系统中的应用. 计算机应用, 2017, 37(增刊Ⅱ): 298-300.Zhang Z Q, Zhong L Z, Yang H P. Application of weather radar in real-time quality control system of hourly gauge precipitation in China. J Comput Appl, 2017, 37(Suppl Ⅱ): 298-300. [29] Qi Y C, Martinaitis S, Zhang J, et al. A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system. J Hydrometeor, 2016, 17(6): 1675-1691. [30] Yeung H Y, Man C, Seed A, et al. Development of a Localized Radar-rain Gauge Co-Kriging QPE Scheme for Potential Use in Quality Control of Real-time Rainfall Data. The Third WMO International Conference on Quantitative Precipitation Estimation and Quantitative Precipitation Forecasting and Hydrology, Nanjing, China, 2010. [31] Yeung H Y, Man C, Chan S T, et al. Application of Radar-rain Gauge Co-Kriging to Improve QPE and Quality Control of Real-time Rainfall Data. Proceedings of the International Symposium on Weather Radar and Hydrology, Exeter, UK, 2011. [32] 丛芳, 刘黎平. 新一代天气雷达与地面雨量资料的综合分析. 气象, 2011, 37(5): 532-539.Cong F, Liu L P. A comprehensive analysis of data from the CINRAD and the ground rainfall station. Meteor Mon, 2011, 37(5): 532-539. [33] 王红艳, 王改利, 刘黎平, 等. 利用雷达资料对自动雨量计实时质量控制的方法研究. 大气科学, 2015, 39(1): 59-67.Wang H Y, Wang G L, Liu L P, et al. Development of a real-time quality control method for automatic rain gauge data using radar quantitative precipitation estimation. Chinese J Atmos Sci, 2015, 39(1): 59-67. [34] 吴书成, 魏爽, 吴京生. 雷达估测降水在区域站降水质控中的应用. 气象科技, 2015, 43(1): 49-52.Wu S C, Wei S, Wu J S. Application of radar precipitation estimation to quality control for regional precipitation. Meteor Sci Technol, 2015, 43(1): 49-52. [35] Seo D J, Breidenbach J P, Johnson E R. Real-time estimation of mean field bias in radar rainfall data. J Hydrol, 1999, 223(3/4): 131-147. [36] Seo D J, Breidenbach J P. Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J Hydrometeor, 2002, 3(2): 93-111. [37] 中国气象局. 地面气象自动观测规范. 北京: 气象出版社, 2020.China Meteorological Administration. Specification for Automatic Observation of Ground Meteorology. Beijing: China Meteorological Press, 2020. [38] 张强, 赵煜飞, 范邵华. 中国国家级气象台站小时降水数据集研制. 暴雨灾害, 2016, 35(2): 182-186.Zhang Q, Zhao Y F, Fan S H. Development of hourly precipitation datasets for national meteorological stations in China. Torrential Rain Disasters, 2016, 35(2): 182-186. [39] 王颖, 刘振. QXT 515—2019气象要素特征值. 2019.Wang Y, Liu Z. QXT 515-2019 Meteorological Element Characteristic Values. 2019. [40] Yang S, Jones P D, Jiang H, et al. Development of a near-real-time global in situ daily precipitation dataset for 0000-0000 UTC. Int J Climatol, 2020, 40(5): 2795-2810. [41] Wang R W, Han W, Tian W H, et al. Blacklist design of AMDAR temperature data and their application in the CMA-GFS. J Trop Meteor, 2021, 27(4): 368-377. [42] 张博, 张芳华, 李晓兰, 等. "23·7" 华北特大暴雨数值预报检验评估. 应用气象学报, 2024, 35(1): 17-32.Zhang B, Zhang F H, Li X L, et al. Verification and assessment of "23·7" severe rainstorm numerical prediction in North China. J Appl Meteor Sci, 2024, 35(1): 17-32. -
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