气象与环境学报2024，Vol.40Issue(5)：40-49,10.DOI:10.3969/j.issn.1673-503X.2024.05.005
1990-2019年中国度假气候舒适日数时空分布特征
Characteristics of spatial-temporal distribution of holiday climate comfortable days over China from 1990 to 2019
摘要
Abstract
This study analyzed the spatio-temporal distribution pattern of the holiday climate comfort days in China from 1990 to 2019 in terms of annual,seasonal,monthly,and typical holiday periods using Holiday Climate Index(HCI),which incorporated five meteorological elements,temperature,precipitation,relative humidity,wind speed,and sunshine hours.The results show that the average annual number of comfortable days for holiday climates in China is 134.3,with the highest occurrence in autumn and the lowest in winter.The monthly and daily distribution of comfortable days exhibits a bimodal curve pattern,with secondary and primary peaks observed in early May to mid-June and early September to mid-October,respectively.The spatial distribution of holiday climate comfortable days is uneven;Yunnan and southern Sichuan exceed 200 days,while the majority of the Qinghai-Tibet Plateau and northern Northeast China have fewer than 100 days.Yunnan,Xinjiang,Guangdong,Shanxi,and He'nan are the top five provinces(autonomous regions)with the highest annual comfortable days for holiday climate,and Yunnan has the most with 207.7 days.The spring,autumn,and winter seasons in Yunnan rank among the top five for holi-day climate comfortable days nationwide.May,September,and October show the broadest range of comfortable holiday climate.During the"May Day"and"National Day"holidays,the probability of comfortable weather ex-ceeds 50% in most regions of China.However,during the"New Year's Day"holiday,the probability of comforta-ble weather exceeds 50% only occurred in South China and Yunnan.关键词
度假气候指数/气候舒适日数/气候特征Key words
Holiday Climate Index(HCI)/Holiday climate comfortable days/Climatic characteristics分类
天文与地球科学引用本文复制引用
王荣,赵珊珊,叶殿秀..1990-2019年中国度假气候舒适日数时空分布特征[J].气象与环境学报,2024,40(5):40-49,10.基金项目
国家重点研发计划(2020YFA0608203)和国家气侯中心科研发展基金共同资助. （2020YFA0608203）
