青海省近20年短时强降水时空变化特征分析  

Analysis of Spatiotemporal Variation Characteristics of Short-duration Heavy Precipitation in Qinghai Province in the Past 20 Years

作　　者：李延平 胡单辉 王宝忠[1] 张嘉男 Li Yanping;Hu Danhui;Wang Baozhong;Zhang Jia’nan(Qinghai Meteorological Information Center,Xining 810001,China)

机构地区：[1]青海省气象信息中心,西宁810001

出　　处：《青海科技》2024年第5期133-139,共7页Qinghai Science and Technology

基　　金：青海省气象局重点项目“青海短时强降水人工智能训练数据集研制”(QXZD2024-03);国家气象信息中心揭榜挂帅项目“青藏高原强对流天气人工智能应用训练基础数据集研制”。

摘　　要：基于青海省2003—2023年的小时降水和2020—2023年分钟降水观测数据收集到的1425个站次的短时强降水个例,对青海省短时强降水时空变化特征进行分析,结果表明:(1)青海省历年可观测短时强降水整体呈逐渐增加的趋势,2016年以来快速增加,2022年发生次数最多。(2)从短时强降水的时间变化特征看,60.84%的个例主要集中在8月;88.6%的个例主要发生在15时至次日04时之间,呈现比较明显的夜雨特征;93.68%的个例小时降水量集中在>20~40 mm;96.1%的个例仅维持1小时,小时内分钟降水量表现为小雨量的较长时间持续。(3)从空间分布来看,海东、西宁、海南、黄南为短时强降水的高发地,多位于东部、东南以及地形变化较剧烈的位置。随着深度学习和大模型的发展,以及人工智能降水与数值模式预报的结合,有望进一步改善短时强降水监测和预报能力。In this paper,using hourly precipitation observation data from 2003 to 2023 and minutely precipitation observation data from 2020 to 2023 of Qinghai Province,1425 cases of short-duration heavy precipitation were collected to analyze the spatiotemporal variation characteristics of short-duration heavy precipitation in Qinghai Province.The results showed that:(1)The occurrence of short-duration heavy precipitation in Qinghai Province has shown an overall increasing trend over the years,with a rapid increase since 2016 and the highest number of occurrences in 2022.(2)In terms of temporal variation characteristics,60.84%of the cases were mainly concentrated in August,and 88.6%occurred between 15:00 and 04:00 the next day,exhibiting a clear nocturnal precipitation characteristic.93.68%of the cases had hourly precipitation amounts concentrated between>20 to 40 mm,and 96.1%of the cases lasted only for one hour,with minute precipitation amounts indicating a longer duration of light rainfall.(3)Spatially,Haidong,Xining,Hainan,and Huangnan were identified as high-incidence areas for short-duration heavy precipitation,mostly located in the eastern,southeastern regions,and areas with significant terrain variations.With the advancement of deep learning and large models,the integration of artificial intelligence with numerical weather prediction models is expected to further improve the monitoring and forecasting capabilities for short-duration heavy precipitation.

关 键 词：短时强降水 时间变化特征 空间分布特征 

分 类 号：P426.6[天文地球—大气科学及气象学]