摘要
利用南京市浦口1981-2017年夏季(5-9月)各类强降水资料,采用趋势分析、突变检验和周期分析等统计方法,从时间和量级2个方面对各类强降水特征展开分析,以期为农业防灾减灾和精细化预报预警提供支撑。结果表明:(1)7月上、中旬是致灾性连续暴雨和大暴雨高发期,100~150 mm的大暴雨发生频率最高,夏季暴雨量无明显突变,但具有显著的6年周期;(2)达暴雨预警级别强降水一般夜间开始,下午明显减弱,最强降水持续时间在12 h左右,短时强降水易发生在中午前后和傍晚;(3)有近4成的预警信号和一半以上的大暴雨都发生在2008-2017年,但小时最大雨量无明显增强;(4)39%的暴雨达到橙色预警标准,但红色仅有1次。地理位置和自然环境是浦口比南京主城区及周边强降水强度大的主要原因。
The paper aims to provide support for the agricultural disaster prevention and mitigation, and for the fine forecast and early warning. Based on the data of diverse strong precipitation in summer(May to September) in Pukou during 1981-2017, we analyzed the characteristics of strong precipitation from time and amounts by adopting the methods of trend analysis, mutation test and the periodic analysis. The results showed that:(1) early and mid of July were peak period of continuous rainstorm and heavy rainstorm, the frequency of heavy rainstorm with 100-150 mm was the highest and the heavy rainstorm had no obvious mutation in summer, but a remarkable 6-year cycle;(2) strong precipitation which reached the level of rainstorm earlywarning often started at the midnight and obviously became weakened in the afternoon;the strong precipitation duration was about 12 hours;the short-time precipitation always happened around the afternoon or in the dusk;(3) 40% of the warning signals and over half of the strong precipitation happened in 2008-2017, however, there was no obvious increase of the maximum precipitation per hour;(4) 39% of the strong precipitation reached the level of orange early-warning, but only one reached red early-warning. The main causes that the strong precipitation intensity in Pukou is stronger than that in urban Nanjing are the location and the natural environment.
作者
陈其旭
季厚瑜
陈旭
Chen Qixu;Ji Houyu;Chen Xu(Puhou Meteorological Bureau,Nanjing 211800)
出处
《中国农学通报》
2019年第33期124-130,共7页
Chinese Agricultural Science Bulletin
基金
中国气象局软科学研究项目“基层气象防灾减灾示范区建设研究”([2014]M18号)
浦口智慧气象防灾系统(PKZC—2015GK—L006)
关键词
强降水
变化趋势
周期分析
影响因素
农业防灾减灾
strong precipitation
change trend
periodic analysis
influence factor
agricultural disaster prevention and mitigation
