Han Feng, Wo Weifeng. Design and implementation of SWAn2.0 platform. J Appl Meteor Sci, 2018, 29(1): 25-34. DOI:  10.11898/1001-7313.20180103.
Citation: Han Feng, Wo Weifeng. Design and implementation of SWAn2.0 platform. J Appl Meteor Sci, 2018, 29(1): 25-34. DOI:  10.11898/1001-7313.20180103.

Design and Implementation of SWAN2.0 Platform

DOI: 10.11898/1001-7313.20180103
  • Received Date: 2017-07-25
  • Rev Recd Date: 2017-12-01
  • Publish Date: 2018-01-31
  • Severe Weather Automatic Nowcasting System 2.0(SWAN2.0) is a short-term nowcasting operational platform of CMA, providing nowcasting products and an early warning product generation tool. SWAN2.0 includes three types of meteorological products. Observation products, mainly composed of radar puzzles and automatic weather station(AWS) observations. Alarm products, including AWS elements alarms and radar echo alarms. Nowcasting products, providing 0-1 h radar echo forecast by COTREC movement vector and the tracking and forecasting of convection storm by SCIT (Storm Cell Identification and Tracking) or TITAN(Thunderstorm Identification, Tracking, Analysis and Nowcasting). SWAN2.0 is based on MICAPS4(Meteorological Information Comprehensive Analysis and Processing System Version 4) development framework, using C/S architecture. The server of SWAN2.0 is a scheduling platform of meteorological algorithm, which is deployed at the provincial meteorological administration, in charge of collecting data, running algorithm, and generating SWAN products. The client of SWAN2.0 is a complete working platform for weather forecasters deployed in national, provincial, and municipal meteorological observatories, which are used to display SWAN products, make analysis and produce weather forecast products. SWAN2.0 adopts new nowcasting technologies, such as three-dimensional variation assimilation retrieval of wind field, QPE(quantity precipitation estimation) by rain cluster, hail identification and meso-scale numerical model application, supporting weather forecasters to extend from traditional short-term weather forecasts and services to short-range and nowcasting forecasts of classified strong convective weather.SWAN2.0 integrates computer technology and forecasting technology to solve short-term forecasting problems. It uses the message queue to decouple business modules to enhance the flexibility and scalability of the platform, and can generate early warning produces automatically from alarm products. The hierarchical structure is adopted to optimize the design of the alarm module, and the alarm module efficiency is improved with pipeline filter model and asynchronous technology.In addition, SWAN2.0 adds two common data models, grid data model and feature data model, creating easy access to local products.In short, SWAN2.0 is not only a operational platform for forecaster but also a set of open data platform and development environment. It provides data services of real-time radar, automatic station and basic short-term nowcasting data, open operating environment and display terminal for the station, and provides support for station localization algorithm development.SWAN2.0 is released in July 2016, and popularized in nationwide. It provides an important foundation and reference for routine nowcasting operation.
  • Fig. 1  Framework of SWAN2.0

    Fig. 2  The algorithm structure of SWAN2.0

    Fig. 3  The design of alarm module in SWAN2.0

    Fig. 4  Display of GRAPES_Meso output

    Fig. 5  Display of overlapping of combined reflectivity and radar feature alarm at 1412 BT 23 Jun 2016

    Fig. 6  Warning signal coordination

  • [1]
    陈明轩, 俞小鼎, 谭晓光, 等.对流天气临近预报技术的发展与研究进展.应用气象学报, 2004, 15(6):754-766. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040693&flag=1
    [2]
    Wilson J W, Crook N A, Mueller C K, et al.Nowcasting thunderstorms:A status report.Bull Amer Meteor Soc, 1998, 79:2079-2099. doi:  10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2
    [3]
    陈明轩, 高峰, 孔荣, 等.自动临近预报系统及其在北京奥运期间的应用.应用气象学报, 2010, 21(4):395-404. doi:  10.11898/1001-7313.20100402
    [4]
    Li P W, Wong W K, Chan K Y, et al. SWIRLS-An Evolving Nowcasting System. Technical Note 100, Hongkong Observatory, 2000.
    [5]
    万玉发, 王志斌, 张家国, 等.长江中游临近预报业务系统(MYNOS)及其应用.应用气象学报, 2014, 24(4):504-512. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20130413&flag=1
    [6]
    万玉发, 张家国, 杨洪平, 等.联合雷达网和卫星定量监测与预报长江流域大范围降水.应用气象学报, 1998, 9(1):94-103. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19980113&flag=1
    [7]
    郑永光, 张小玲, 周庆亮, 等.强对流天气短时临近预报业务技术进展与挑战.气象, 2010, 36(7):33-42. doi:  10.7519/j.issn.1000-0526.2010.07.008
    [8]
    赵栋, 郭煜, 寿绍文, 等.SWAN系统在一次暴雨天气过程分析中的应用.气象科技, 2013, 41(2):326-333. http://d.old.wanfangdata.com.cn/Periodical/qxkj201302022
    [9]
    吕晓娜, 牛淑贞, 袁春风, 等.SWAN中定量降水估测和预报产品的检验与误差分析.暴雨灾害, 2013, 32(2):142-150. https://www.wenkuxiazai.com/doc/c174114aa76e58fafab003e5.html
    [10]
    李俊, 支树林, 郭艳, 等.SWAN系统雷达定量降水估测产品在江西的应用.气象与减灾研究, 2012, 35(2):61-66. https://www.wenkuxiazai.com/doc/77613b72168884868762d686.html
    [11]
    高嵩, 毕宝贵, 李月安, 等.MICAPS4预报业务系统建设进展与未来发展.应用气象学报, 2017, 28(5):513-531. doi:  10.11898/1001-7313.20170501
    [12]
    田付友, 郑永光, 张涛, 等.短时强降水诊断物理量敏感性的点对面检验.应用气象学报, 2015, 26(4):385-396. doi:  10.11898/1001-7313.20150401
    [13]
    吴涛, 万玉发, 沃伟峰, 等.SWAN系统中雷达反射率因子质量控制算法及其应用.气象科技, 2013, 41(5):809-817. http://www.cqvip.com/QK/93750X/201305/47714805.html
    [14]
    陈雷, 戴建华, 陶岚.一种改进后的交叉相关法(COTREC)在降水临近预报中的应用.热带气象学报, 2009, 25(1):117-122. http://mall.cnki.net/magazine/Article/RDQX200901017.htm
    [15]
    胡胜, 罗兵, 黄晓梅, 等.临近预报系统(SWIFT)中风暴产品的设计及应用.气象, 2010, 36(1):54-58. doi:  10.7519/j.issn.1000-0526.2010.01.008
    [16]
    郑永光, 林隐静, 朱文剑, 等.强对流天气综合监测业务系统建设.气象, 2013, 39(2):234-240. doi:  10.7519/j.issn.1000-0526.2013.02.013
    [17]
    郑永光, 周康辉, 盛杰, 等.强对流天气监测预报预警技术进展.应用气象学报, 2015, 26(6):641-657 doi:  10.11898/1001-7313.20150601
    [18]
    Rinehart R E, Garvey E T.Three dimensional stormmotion detection by conventional weather radar.Nature, 1978, 273:287-289. doi:  10.1038/273287a0
    [19]
    王艳春, 王红艳, 刘黎平.三维变分方法反演风场的效果检验.高原气象, 2016, 35(4):1087-1101. http://d.old.wanfangdata.com.cn/Periodical/gyqx201604022
    [20]
    Qiu C J, Shao A M, Liu S, et al.A two-step variational method for three-dimensional wind retrieval from single Doppler radar.Meteor Atmos Phys, 2006, 91(1):1-8. doi:  10.1007/s00703-004-0093-8.pdf
    [21]
    Shao A, Qiu C, Liu L.Kinematic structure of a heavy rain event from dual-Doppler radar observations.Adv Atmos Sci, 2004, 21(4):609-616. doi:  10.1007/BF02915728
    [22]
    吴林林, 刘黎平, 郑媛媛, 等.基于SWAN的冰雹探测算法研究.高原气象, 2014, 33(3):823-831. doi:  10.7522/j.issn.1000-0534.2012.00037
    [23]
    张秉详, 李国翠, 刘黎平, 等.基于模糊逻辑的冰雹天气雷达识别算法.应用气象学报, 2014, 25(4):414-426. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20140404&flag=1
  • 加载中
  • -->

Catalog

    Figures(6)

    Article views (3160) PDF downloads(620) Cited by()
    • Received : 2017-07-25
    • Accepted : 2017-12-01
    • Published : 2018-01-31

    /

    DownLoad:  Full-Size Img  PowerPoint