中国农业气象 ›› 2024, Vol. 45 ›› Issue (11): 1314-1324.doi: 10.3969/j.issn.1000-6362.2024.11.006

• 农业生态环境栏目 • 上一篇    下一篇

春茶萌芽期复杂地形下冷池气温特征及易发区识别

范辽生,杨军,洪萍,黄海涛,肖晶晶   

  1. 1. 杭州市气象局,杭州 310051;2. 杭州市农业科学研究院茶叶研究所,杭州 310024;3. 浙江省气候中心,杭州 310057
  • 收稿日期:2023-11-23 出版日期:2024-11-20 发布日期:2024-11-12
  • 作者简介:范辽生,E-mail:hz_reader@126.com
  • 基金资助:
    浙江省重点研发计划项目(2021C02036);浙江省基础公益研究计划项目(LGF22D050007);杭州市气象局气象科技计划项目(QX202115)

Temperature Characteristics and Prone Identification Region of Cold Air Pool under Complex Terrain during Spring Tea Budding Period

FAN Liao-sheng, YANG Jun, HONG Ping, HUANG Hai-tao, XIAO Jing-jing   

  1. 1.Hangzhou Meteorological Bureau, Hangzhou 310051,China; 2. Tea Research Institute, Hangzhou Academy of Agricultural Science and Technology, Hangzhou 310024; 3. Zhejiang Climate Center, Hangzhou 310057
  • Received:2023-11-23 Online:2024-11-20 Published:2024-11-12

摘要:

以西湖龙井主要产区研究区域,利用2021研究区内29个自动气象站春茶萌芽期气温和雷达探空逆温数据,分析典型冷池易发区的冷池气温特征,探索基于DEM自动识别复杂地形下冷池易发区方法。结果表明:2021220−331,西湖龙井产区典型冷池易发区的冷池日发生频率为45%。冷池日发生频率和强度与天气类型有关,晴天和多云天气条件下极易出现冷池寡照天气下较少发生强冷池日多出现于晴天条件。冷池增了逆温频率和强度,与平地相比,温频率增加了23%,平均最大逆温强度增加了1.26℃·100m1一次典型的强冷池过程包含了形成加强、维持和消弱消散3个阶段消弱消散阶段谷底的最大小时升温幅度达到11.3℃,比形成加强阶段最大小时降温幅度(7.2℃)高4.1℃由DEM数据中的坡度、相对高度百分率地形曲率3个地形因子构建判别指标识别冷池易发区,识别效果较好冷池易发区和非易发区站点的识别准确率分别为80%78%研究区内茶园有约26%面积分布在冷池易发区内,极端低温和剧烈升温过程影响,更易在春茶萌芽期导致春茶受霜冻危害。

关键词: 冷池, 气温特征, DEM, 识别方法, 春茶

Abstract:

Typical temperature characteristic of cold air pool (CAP) in the main producing areas of Xihu Longjing plantation was analyzed using data from 29 automatic meteorological stations and radar sounding inversion data during spring tea budding period in 2021. A method was explored to automatically identify areas prone CAP under complex terrain. The results showed that the occurrence frequency of CAP day was 45% in typical prone CAP areas of Xihu Longjing plantation from February 20 to March 31, 2021. The occurrence frequency and intensity of CAP were related to weather types. CAP was more likely to occur on sunny and cloudy weather conditions, less on overcast weather conditions, and strong CAP days predominantly appearing under clear sky conditions. CAP increased the frequency and intensity of inversions, with an increase of 23% in inversion frequency, and an increase of 1.26℃ per 100m in average maximum inversion intensity compared to flat areas. A typical daily variation of CAP included three stages: formation and strengthening, maintenance, and weakening and dissipation, with the maximum hourly temperature increase at the valley during the weakening and dissipation stage reaching 11.3℃·h1, which was 4.1℃·h1 higher than the maximum hourly temperature decrease during the formation and strengthening stage (7.2℃·h1). To identify the CAP prone areas, three terrain factors in DEM data including slope, percentile of height relative to surroundings, and terrain curvature were constructed as discriminant indicators showing good identification effectiveness. Coincidence rates of stations located in CAP prone and non prone areas were 80% and 78% respectively. About 26% of the tea plantation in study area was located in CAP prone areas, making them more susceptible to spring tea frost damage during the budding period due to extreme low temperatures and rapid warming processes.

Key words: Cold air pool, Temperature characteristics, DEM, Identification, Spring tea