干旱区研究 ›› 2022, Vol. 39 ›› Issue (1): 54-63.doi: 10.13866/j.azr.2022.01.06

• 应用气候 • 上一篇    下一篇

全国降水天气现象平行观测对比分析

马宁1,2,3(),任芝花4(),王妍4,刘娜4,曹宁1,2   

  1. 1.中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室,宁夏 银川 750002
    2.宁夏气象防灾减灾重点实验室,宁夏 银川 750002
    3.宁夏气象信息中心,宁夏 银川 750002
    4.国家气象信息中心,北京 100081
  • 收稿日期:2021-01-16 修回日期:2021-04-02 出版日期:2022-01-15 发布日期:2022-01-24
  • 通讯作者: 任芝花
  • 作者简介:马宁(1983-),女,高级工程师,主要从事气象数据分析处理工作. E-mail: mn_2001@126.com
  • 基金资助:
    国家自然科学基金项目(91744209);中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室项目(CAMP-201910);中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室项目(CAMP-202103)

Comparative analysis of parallel observations of precipitation phenomena in China

MA Ning1,2,3(),REN Zhihua4(),WANG Yan4,LIU Na4,CAO Ning1,2   

  1. 1. Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, China Meteorological Administration, Yinchuan 750002, Ningxia, China
    2. Ningxia Key Lab of Meteorological Disaster Prevention and Reduction, Yinchuan 750002, Ningxia, China
    3. Ningxia Meteorological Information Center, Yinchuan 750002, Ningxia, China
    4. National Meteorological Information Center, Beijing 100081, China
  • Received:2021-01-16 Revised:2021-04-02 Online:2022-01-15 Published:2022-01-24
  • Contact: Zhihua REN

摘要:

采用2017-08—2018-08全国2363个气象站降水现象平行观测对比观测数据,分别从数据完整性和准确性方面对雨、雪、毛毛雨、冰雹、雨夹雪5种降水现象自动观测数据进行了对比分析。结果表明:(1) 雨、雪、毛毛雨现象的过程捕获率最高,分别为66.9%、69.2%、50.1%,冰雹的小时捕获率最高,为51.8%,雨夹雪的各捕获率指标均较低。(2) 毛毛雨的漏报率最高为65.9%,雪的漏报率次之为35.6%,冰雹的漏报率最低为16.2%;毛毛雨、雨的错报率较低,雨夹雪和冰雹的错报率较高,毛毛雨、冰雹错报为雨的比例较高,雨错报成毛毛雨的比例较高,雪错报成毛毛雨和雨的比例较高,而雨夹雪则常常是毛毛雨、雨、雪交替出现;毛毛雨和冰雹的空报站点较多。(3) 降水现象仪对降水现象的识别可达到分钟级,但与人工观测降水现象相比,存在漏报、错报和空报情况,需要在气象站数据采集端进行质量控制,不断优化降水现象识别算法,并结合其他天气要素进行降水现象综合判识,以提高仪器对降水天气现象的捕获率,降低漏报率、错报率和空报率。

关键词: 降水现象, 平行观测, 数据准确性, 捕获率, 漏报率, 错报率, 空报率

Abstract:

Manual and automatic precipitation data (i.e., parallel observation data) collected at 2363 meteorological stations in China between August 2017 and August 2018 were used to analyze the integrity and accuracy of automatic observed data (singular value removed). Firstly, the comparison of the capture rate indices of rain, snow, sleet, hail, and drizzle obtained from parallel observations shows that, during the entire precipitation process, rain, snow, and drizzle had a higher capture rate than sleet and hail had, with values of 66.9%, 69.2%, and 50.1%, respectively; and the hourly capture rate of hail (51.8%) was the highest. Secondly, as for miss rate of phenomenon automatic observed, the highest value was recorded for drizzle (65.9%), the second-highest for snow (35.6%), and the lowest for hail (16.2%). Thirdly, the misreported rates of drizzle, rain, and snow were lower than those of sleet and hail: Analogous percentages of rain were misreported as drizzle, higher percentages of drizzle or snow were misreported as rain, and also higher percentages of snow were misreported as drizzle or rain. The last statistical result is that the index of empty report rate shows that drizzle and hail were not captured at a substantial number of meteorological stations through automatic observations. Compared with manual observations, a portion of precipitation phenomena was missed, misreported, or even not captured by automatic observations using disdrometers, although these instruments have a minute level temporal resolution. Brook no delay, quality control procedures should be carried out once the data are collected by disdrometers, the algorithm for phenomenon identification should be continuously optimized, and the comprehensive identification of precipitation phenomena should be carried out by combining other weather elements.

Key words: precipitation phenomenon, parallel observation, data accuracy, capture rate, miss rate, misreported rate