干旱气象 ›› 2022, Vol. 40 ›› Issue (1): 146-155.DOI: 10.11755/j.issn.1006-7639(2022)-01-0146

• 业务技术应用 • 上一篇    下一篇

基于SCTP-RF算法的甘肃省短期定量降水客观预报方法研究

刘娜(), 黄武斌(), 杨建才, 王基鑫, 王一丞, 张君霞   

  1. 兰州中心气象台,甘肃 兰州 730020
  • 收稿日期:2021-04-06 修回日期:2021-08-19 出版日期:2022-02-28 发布日期:2022-02-28
  • 通讯作者: 黄武斌
  • 作者简介:刘娜(1994— ),女,甘肃武威人,硕士,助理工程师,主要从事中短期天气预报及客观预报方法研究. E-mail: liunavip666@163.com
  • 基金资助:
    甘肃省气象局人才专项(2122rczx);干旱气象科学研究基金(IAM202011);甘肃省科技计划项目(20YF3FA012);西北区域人工影响天气能力建设项目研究试验项目(ZQC-R18208);甘肃对流性暴雨预报预警关键技术创新团队共同资助(GSQXCXTD-2020-01)

Objective forecast method of short-term quantitative precipitation in Gansu Province based on SCTP-RF algorithm

LIU Na(), HUANG Wubin(), YANG Jiancai, WANG Jixin, WANG Yicheng, ZHANG Junxia   

  1. Lanzhou Central Meteorological Observatory, Lanzhou 730020, China
  • Received:2021-04-06 Revised:2021-08-19 Online:2022-02-28 Published:2022-02-28
  • Contact: HUANG Wubin

摘要:

基于欧洲中期天气预报中心(ECMWF)的精细化数值预报产品、中国气象局下发的降水指导产品(TP_CMA)及甘肃省340个气象站点降水实况数据,利用泰森多边形与K-means空间聚类方法(spatial cluster and Tyson polygon,SCTP),对2017—2019年4—9月甘肃省340站降水资料进行客观分区。在此基础上,采用随机森林算法(random forest,RF),筛选出与降水相关的物理量因子构建模型,开展甘肃省短期定量降水客观预报订正试验,并进行预报效果检验。结果表明:(1)甘肃省4—9月降水客观分区依次为7、6、14、13、14和11个。(2)就晴雨预报而言,SCTP-RF订正产品对甘肃省汛期的晴雨预报能力较TP_CMA指导产品和ECMWF模式产品有一定提升,提升幅度分别为6.1%、4.2%;在空间上,SCTP-RF算法对甘肃省340站的晴雨预报均具有一定的订正能力,大部分站点晴雨预报准确率提升了5%,特别是河东地区。(3)在分级降水预报中,SCTP-RF订正产品对中雨和大雨的预报能力均优于TP_CMA指导产品和ECMWF模式产品,且全省大部的订正效果较好,特别是河东中部及陇东南地区,但在强降水过程中对小雨和暴雨的预报订正不稳定,尤其是陇东南地区的小雨。

关键词: 降水及分级, 短期预报, 空间聚类, 随机森林, 预报订正及检验

Abstract:

Based on the refined numerical prediction products of the European Center of Medium-range Weather Forecast (ECMWF), precipitation guidance products from China Meteorological Administration (TP_CMA) and precipitation observation data at 340 meteorological stations of Gansu Province, the objective divisions of precipitation at 340 meteorological stations of Gansu Province from April to September during 2017-2019 were done by using spatial cluster and Tyson polygon (SCTP) approach. On this basis, the physical quantity factors related to precipitation were selected and used to build prediction model by using random forest (RF) algorithm, and the correction experiment of short-term quantitative precipitation objective forecast in Gansu Province was carried out, the forecast effect was verified. The results are as follows: (1) There were 7, 6, 14, 13, 14 and 11 precipitation regions in sequence from April to September in Gansu Province. (2) In terms of rain probability forecast, the forecast ability of SCTP-RF products in flood season (from June to August) in Gansu Province was better than that of TP_CMA guidance products and ECMWF model products, and the prediction accuracy improved by 6.1% and 4.2%, respectively. In space, SCTP-RF products had a certain ability to correct rain probability forecast at all stations of Gansu Province, and the prediction accuracy at most stations improved by 5%, especially in the east of Yellow River in Gansu (Hedong area). (3) For graded precipitation forecast, the forecast ability of SCTP-RF products to moderate rain and heavy rain was superior to TP_CMA guidance products and ECMWF model products, and the correction effect at most stations was better, especially in the middle part of Hedong and southeastern Gansu. However, the correction ability to light rain and rainstorm forecast was unstable during the heavy rainfall processes, especially to light rain in southeastern Gansu.

Key words: precipitation and classification, short-term forecast, spatial clustering, random forest, forecast correction and test

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