Sun Chao, Huo Qing, Ren Zhihua, et al. Design and implementation of surface meteorological data statistical processing system. J Appl Meteor Sci, 2018, 29(5): 630-640. DOI:  10.11898/1001-7313.20180511.
Citation: Sun Chao, Huo Qing, Ren Zhihua, et al. Design and implementation of surface meteorological data statistical processing system. J Appl Meteor Sci, 2018, 29(5): 630-640. DOI:  10.11898/1001-7313.20180511.

Design and Implementation of Surface Meteorological Data Statistical Processing System

DOI: 10.11898/1001-7313.20180511
  • Received Date: 2018-06-06
  • Rev Recd Date: 2018-08-10
  • Publish Date: 2018-09-30
  • Statistical products of surface meteorological data (SMD) are among the most-frequently-used data in meteorological research and operations. As the improvement of surface meteorological observation system over China, statistics of SMD have encountered problems such as large number of sites, wide variety of elements, and complexity of statistical strategy. With typical features of big data, it's possible for SMD to serve more precise and efficient operations nowadays, which is obviously beyond the capability of traditional serial processing framework.Aiming at precise and efficient statistic processing of data from more than 60000 surface weather stations, a statistical processing system for SMD is built based on big data technology. Compared to traditional serial processing framework, efficiency of the system has increased by more than 10 times and more statistics and function are provided, such as fast calculation, rolling update of statistical values according to late-arriving data and corrected information, and arbitrary time scale statistics. Storm distributed flow processing technology is applied in the system to realize efficient statistical calculations. Big data message transmission and cache technology are also applied to ensure the system's high efficiency and stability. Modular design framework ensures strong extensibility of the system, based on which statistics, quality control and evaluation algorithms are extended to varieties of data, e.g., upper-air, radiation, oceanic and aircraft measurements. The system is deployed at national meteorology department and its products are synchronously applied at the provincial level, for this layout ensures data consistency.The system is incorporated into China Integrated Meteorological Information Sharing System (CIMISS) and become its core data processing framework. The system provides more than 800 real-time multi-scale SMD statistical values to serve meteorological users and the public through CIMISS data unified service interface since January 2017. Based on data access logs, monthly access of daily SMD statistics reach 19.51 million times in 2017, ranking the 3th among over 400 data or products, playing important roles in weather monitoring, forecasting and warning, meteorological decision, public service and climate research.In the future, the technical framework and algorithm module of the system will be integrated into the processing pipeline of meteorological large data cloud platform, with further optimization of the computational topology for full use of computing resources, which can increase convergence time for distributed node processing results. To further improve the efficiency of statistical processing, the launching mechanism of this operation can be changed from periodic to automatic scheduling based on the trigger of observed data integrity.
  • Fig. 1  Framework of Surface Meteorological Data Statistical Processing System

    Fig. 2  Storm architecture

    Fig. 3  Rolling updating process and average consuming time

    Fig. 4  Correction proportion of daily elements statistics in Jul 2017

    Fig. 5  Real rate of common surface element statistics

    (a)national surface weather station, (b)regional automatic weather station for accessment

    Fig. 6  Application of big data message transmission and cache technology

    Fig. 7  Application of statistical products to meteorological services

    Fig. 8  Application of the multi-scale statistics of temperature

    Fig. 9  Number of surface gale(more than 17 m/s) days over China in 2017

    (a)national surface weather station, (b)national surface weather station and regional automatic weather station

    Table  1  Data amount used for target statistics

    统计尺度 数据源 记录数/104 数据量/GB
    小时观测数据 144 12
    日统计值小时观测数据 750 64
    日统计值小时观测数据 1500 128
    日统计值小时观测数据 4500 300
    日统计值月统计值 4800 500
    月统计值分钟观测数据 130000 3600
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    Table  2  Timing start time and product service timeliness

    统计产品(按统计尺度划分) 定时启动时间 产品服务时间
    每日21:00, 第2日08:40(更新天气现象统计) 21:15, 08:55
    每月1日、6日、11日、16日、21日、26日09:00 09:10
    每月1日、11日、21日09:10 09:20
    每月1日09:20 10:00
    每年的3月、6月、9月、12月的1日10:00 11:00
    每年1月1日11:00 13:00
    注:表中时间均为北京时,下同;产品服务时间定义为该统计产品可提供用户检索的时间;天气现象要素统计的数据源包括人工审定后的地面气象观测日数据文件,因此,日统计项中天气现象要素统计计算在第2日收到日数据文件后定时启动。
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    Table  3  Download times of surface meteorological data statistics in national operational systems(Top 10)

    序号 业务系统名称 年下载量/GB 年下载次数
    1 中国天气网 1378.5 34412772
    2 中国气象局公共气象服务中心一体化加工平台 728.3 146352
    3 农业气象业务系统(CAgMSS) 288.6 373152
    4 预警信息发布数据支撑系统 231.8 203982
    5 北京市空气质量预报预警平台 138.4 23326
    6 气象服务信息系统(MESIS) 47.2 8760
    7 气候信息处理与分析系统(CIPAS) 25.3 723261
    8 中国气象数据网 24.8 164611
    9 气象灾害信息管理系统 20.3 99759
    10 中国兴农网 16.2 1528
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    • Received : 2018-06-06
    • Accepted : 2018-08-10
    • Published : 2018-09-30

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