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The Design and Implementation of Stream Processing for Data of Ground Automatic Weather Stations
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摘要: 针对观测密度和频次日益增加的海量地面自动气象站数据, 在气象大数据云平台(天擎)中设计了基于Storm的实时流式处理, 利用大规模并行处理的优势提高地面自动气象站数据的处理时效。在流式处理中, 设计处理拓扑直接解码标准格式的数据消息;消息确认采用手工确认的方式, 将数据解码组件锚定数据接入组件, 实现每条数据的可靠处理;数据解码时进行字节校验和时间检查等, 过滤异常数据;应用批量加定时的发送策略, 解决海量监控信息发送气象综合业务实时监控系统(天镜)的问题;集群部署时保留部分剩余资源, 有效应对单节点异常。应用效果表明:国家气象站小时数据的服务时效由全国综合气象信息共享系统(CIMISS)的175 s提高至天擎的78 s, 约6×104个区域气象站小时数据的服务时效由CIMISS的5 min提高至天警的2 min, 实况分析系统将数据源切换至天擎后, 相同时间检索可获取的站点数量较CIMISS增加1倍。2021年12月基于Storm的流式处理与天擎一同在国省业务化运行, 实现了长期稳定运行, 为MICAPS4、SWAN2.0、实况分析系统等用户提供高效稳定的地面自动气象站数据。Abstract: To process the high-density and high-frequency mass data generated by ground automatic weather stations, a real-time stream processing system based on Storm is designed and implemented in the Meteorological Big Data Cloud Platform (Tianqing). It leverages the advantages of large-scale parallel computing to enhance processing speed. For BUFR messages, a Storm topology is designed to process the standardized BUFR format data transmitted by RabbitMQ directly on the service, reducing the intermediate steps from transmission to processing of observations. In the spout design, the manual confirmation mode of RabbitMQ messages is adopted to ensure that each message is effectively processed. In the decoding process, bolt is anchored to the spout using message identification (ID) to ensure reliable processing of each message. Format and time checks are performed during data decoding to filter out abnormal data. A batch timing monitoring strategy is applied to address the issue of data ingestion loss caused by port occupancy during extensive monitoring data transmission. A startup strategy with a configurable number of spout and bolt is designed for quick optimization and adjustment based on system resources. During cluster deployment, some resources are reserved to enable automatic task migration without disrupting business operations in case of node corruption within the cluster. System design involves automatically reconnecting message queues and databases to enhance system stability and enable self-healing capabilities. Application results show that the service efficiency of 2442 national stations has decreased from 175 s with CIMISS to 78 s with Tianqing. Additionally, the service efficiency of hourly data from over 60000 regional stations has decreased from 5 min with CIMISS to 2 min with Tianqing. After switching the data source of the ART (analysis of real time) system to Tianqing, the number of stations that can be retrieved simultaneously is doubled compared to CIMISS. It can effectively improve the quality of ART live products while keeping other conditions unchanged. By implementing specialized stream processing, it can effectively handle various business scenarios where data access process of the provincial Tianqing ground automatic weather stations differ from that of other provinces. It enables the provincial Tianqing to quickly process nationwide data from ground automatic weather stations. In December 2021, Storm-based stream processing is implemented in the national and provincial meteorological information departments alongside Tianqing. It has been running smoothly over two years, delivering reliable ground automatic weather station data to users, including MICAPS4, SWAN2.0, ART systems and others.
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图 1 2023年3月1日00:50—02:10地面自动气象站数据的逐分钟消息数量
Fig. 1 Message number of observations per minute from ground automatic weather stations from 0050 UTC to 0210 UTC on 1 Mar 2023
图 1 2023年3月1日00:50—02:10地面自动气象站数据的逐分钟消息数量
Fig. 1 Message number of observations per minute from ground automatic weather stations from 0050 UTC to 0210 UTC on 1 Mar 2023
图 2 地面自动气象站数据处理速度与解码入库组件数量关系
Fig. 2 Relationship between processing speed of ground automatic weather station data and bolt quantity
图 2 地面自动气象站数据处理速度与解码入库组件数量关系
Fig. 2 Relationship between processing speed of ground automatic weather station data and bolt quantity
图 3 区域气象站小时数据与区域气象站分钟数据同时处理速度与解码入库组件数量关系
Fig. 3 Relationship between regional station hourly data and minutely data simultaneous processing speed and bolt quantity
图 3 区域气象站小时数据与区域气象站分钟数据同时处理速度与解码入库组件数量关系
Fig. 3 Relationship between regional station hourly data and minutely data simultaneous processing speed and bolt quantity
图 4 2021年12月29日24个时次平均的国家气象站小时数据天擎和CIMISS的入库率随时间变化
Fig. 4 Comparison of data insert rate changes between Tianqing and CIMISS for 24 samples of national station hourly data on 29 Dec 2021
图 4 2021年12月29日24个时次平均的国家气象站小时数据天擎和CIMISS的入库率随时间变化
Fig. 4 Comparison of data insert rate changes between Tianqing and CIMISS for 24 samples of national station hourly data on 29 Dec 2021
图 5 2021年5月23日24个时次平均的区域气象站小时数据天擎和CIMISS的入库率随时间变化
Fig. 5 Comparison of data insert rate changes between Tianqing and CIMISS for 24 samples of regional station hourly data on 23 May 2021
图 5 2021年5月23日24个时次平均的区域气象站小时数据天擎和CIMISS的入库率随时间变化
Fig. 5 Comparison of data insert rate changes between Tianqing and CIMISS for 24 samples of regional station hourly data on 23 May 2021
表 1 地面自动气象站数据入库数据表说明
Table 1 Description of data insert tables for ground automatic weather station data
数据名称 天擎入库表名 国家气象站小时数据 中国地面小时原始报告表 中国地面小时表 全球地面小时表 中国地面日值表 中国地面日照表 重要天气表 国家气象站分钟数据 中国地面分钟原始报告表 中国地面分钟降水表 地面分钟全要素表 区域气象站小时数据 中国地面小时原始报告表 中国地面小时表 区域气象站分钟数据 中国地面分钟原始报告表 中国地面分钟降水表 地面分钟全要素表 
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表 1 地面自动气象站数据入库数据表说明
Table 1 Description of data insert tables for ground automatic weather station data
数据名称 天擎入库表名 国家气象站小时数据 中国地面小时原始报告表 中国地面小时表 全球地面小时表 中国地面日值表 中国地面日照表 重要天气表 国家气象站分钟数据 中国地面分钟原始报告表 中国地面分钟降水表 地面分钟全要素表 区域气象站小时数据 中国地面小时原始报告表 中国地面小时表 区域气象站分钟数据 中国地面分钟原始报告表 中国地面分钟降水表 地面分钟全要素表
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表 2 国家级天擎地面自动气象站数据处理拓扑说明
Table 2 Topology description of national Tianqing ground automatic weather station processing
数据类型 每小时上传数据次数 工作进程数量 数据接入组件数量 解码入库组件数量 国家气象站小时数据 1 12 12 36 国家气象站分钟数据 60 6 6 36 区域气象站小时数据 1 24 24 240 区域气象站分钟数据 12 24 20 300
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表 2 国家级天擎地面自动气象站数据处理拓扑说明
Table 2 Topology description of national Tianqing ground automatic weather station processing
数据类型 每小时上传数据次数 工作进程数量 数据接入组件数量 解码入库组件数量 国家气象站小时数据 1 12 12 36 国家气象站分钟数据 60 6 6 36 区域气象站小时数据 1 24 24 240 区域气象站分钟数据 12 24 20 300
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表 3 2023年3月中国气象局各直属单位通过业务系统获取地面自动气象站小时数据情况汇总
Table 3 Summary of ground automatic weather stations hourly data from systems by various institutions of China Meteorological Administration in Mar 2023
单位 系统名称 访问次数 数据量/GB 国家气象信息中心 天擎实况 17844182 22739.5 中国气象科学研究院 东亚区域再分析及智能预报竞赛系统 753707 9400.8 国家气象中心 智能网格预报处理系统 603944 3305.1 气象探测中心 综合气象观测数据质量控制系统_天衡天衍 372840 350.6 气象干部培训学院 短临预警技能与素质综合训练系统 247207 1580.8 人工影响天气中心 人影效果评估系统 113491 1695.5 国家气候中心 气候变化影响评估与服务系统 42591 8.5 国家卫星气象中心 卫星天气应用平台(SWAP) 22425 54.4 公共气象服务中心 国家级交通气象服务业务 13647 229.5 地球系统数值预报中心 GRAPES数值预报业务系统 5517 44.8
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表 3 2023年3月中国气象局各直属单位通过业务系统获取地面自动气象站小时数据情况汇总
Table 3 Summary of ground automatic weather stations hourly data from systems by various institutions of China Meteorological Administration in Mar 2023
单位 系统名称 访问次数 数据量/GB 国家气象信息中心 天擎实况 17844182 22739.5 中国气象科学研究院 东亚区域再分析及智能预报竞赛系统 753707 9400.8 国家气象中心 智能网格预报处理系统 603944 3305.1 气象探测中心 综合气象观测数据质量控制系统_天衡天衍 372840 350.6 气象干部培训学院 短临预警技能与素质综合训练系统 247207 1580.8 人工影响天气中心 人影效果评估系统 113491 1695.5 国家气候中心 气候变化影响评估与服务系统 42591 8.5 国家卫星气象中心 卫星天气应用平台(SWAP) 22425 54.4 公共气象服务中心 国家级交通气象服务业务 13647 229.5 地球系统数值预报中心 GRAPES数值预报业务系统 5517 44.8
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