摘要
为评价彭州旅游气候资源,利用2012-2021年41个气象观测站(1个国家常规地面观测站及40个区域站)逐日平均气温、最高气温、最低气温、降水量、风速和日照时数等资料,通过温湿、风效、着衣指数建立综合舒适度模型.结果表明:温湿指数山、丘、坝区最适月为6、5、4月份,A等级占87％、87％、53％;风效指数峰值在8月上中旬,较温湿指数滞后10～20 d,最适月为:坝区(3、10、11月份)、山区(4、5、10月份)、丘区(4、10月份);着衣指数低谷在7月下旬,峰值丘区12月下旬,山、坝区1月上旬;综合舒适度指数呈"M"型分布,坝、丘、山区最适月在4月份和10月份、5月份和9月份、6月份和8月份,5-8月份丘坝区指数显著降低,山区波动不大.夏季山区综合舒适度最佳,显著优于丘、坝区.
Abstract
In order to evaluate the tourism climate resources in Pengzhou City,the daily average temperature,max-imum temperature,minimum temperature,precipitation,wind speed and sunshine hours of 41 meteorological ob-servation stations(1 national conventional ground observation station and 40 regional stations)from 2012 to 2021 were used.Through the temperature-humidity index,wind effect index and index of clothing,a comprehensive comfort model is established to evaluate Pengzhou tourism.The results show that:the optimum months of temper-ature-humidity index,mountain,hill and dam area are June,May and April,and the proportion of grade A is 87％,87％and 53％respectively.The peak value of wind effect index is in early and middle August,which is 10-20 days later than that of temperature-humidity index.The optimum months are:dam area(March,October and No-vember),mountain area(April,May and October)and hilly area(April and October).Clothing index trough is in late July;the maximum value of hilly area is in late December;mountain and dam areas are in early January.The comprehensive comfort index showed an'M'type distribution.The most suitable months for dam,hill and moun-tain areas are April and October,May and September,June and August respectively.The index of hill and dam ar-ea decreased significantly from May to August,and the mountain area did not change much.In summer,the com-prehensive comfort of mountainous areas is the best,which is significantly better than that of hills and dams.Mountainous areas are very suitable for tourism in summer.
基金项目
成都市气象局业务科技研究课题(2022-11137)
四川省自然科学基金(2022NSFSC0208)
高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金(SCQXKJYJXMS202210)
甘肃省自然科学基金重点项目(21JR7RA694)
干旱气象科学研究基金(IAM202004)
NETL
NSTL
万方数据