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作 者:张延伟[1,2,3] 葛全胜[1] 魏文寿[4] 郑景云[1]
机构地区:[1]中国科学院地理科学与资源研究所,北京100101 [2]商丘师范学院环境与规划学院,河南商丘476000 [3]山西师范大学地理科学学院,山西临汾041000 [4]中国科学院新疆生态与地理研究所
出 处:《地理科学》2015年第6期765-772,共8页Scientia Geographica Sinica
基 金:973计划项目(2010CB950101);中国科学院战略性先导科技专项项目(XDA05080100);国家科技支撑计划项目(2012BAC23B01);河南省教育厅项目(2014-qn-151);国家社会科学基金项目(14CJY077)资助
摘 要:在实际的气象观察中,受站点迁移、城市化及仪器更换等影响,气象观测数据往往存在不均一性。这种不均一性会掩盖气候变化的真相、造成气候变化诊断结果的失真。因此,观测数据序列均一化具有重要的科学和实际意义。选择1961~2010年期间北疆地区37个气象站点(其中14个站点发生过大的迁移,迁移次数达17次之多),首先以乌鲁木齐站点为例子,说明新的HOMR-HOM方法数据断点检测和数据订正过程。然后,对北疆地区逐日最高气温和最低气温进行均一化处理。结果表明:1新的均一化HOMR-HOM方法能较好的检测断点和订正北疆地区的逐日气温数据;2经过均一化处理,北疆地区最高气温观测数据比均一化后数据高,最低气温观测数据比均一化后数据低。Human activities and the environment are greatly affected by climate and weather extremes. A growing interest in extreme climate events is motivated by the vulnerability of our society to the impacts of such events. In the world, the occurrence of flood over the seven big river valleys is of high frequency, and both flood and geological disasters increased due to the increase of intense precipitation events and the consequent increase of their concentration degree. In practice, climate data is inhomogenous meteorological observations series in northern Xinjiang. Climate data is affected by meteorological site migration, meteorological instruments to replace, change the number of observations, urbanization, and so on. In present study, we applied HOMER-HOM methods to detect and adjust the inhomogeneities of daily temperature series. Based on the HOMER-HOM method, we analyzed the inhomogeneities in daily maxima and minimum temperature series at Urumqi station caused by relocations in 1976, 1999 and 2002. Comper of Meta data, we find Urumqi station relocation in 1975 and in 1999, instrument replacement in 2003. It shows that the HOMER-HOM method is a good effect method. The adjusted series exhibited a long-term daily maxima and minimum temperature series for the annual mean series during 1961-2010, in which the daily maxima temperature series bias is high by comparison of correct data and the daily minimum temperature series bias is low by comparison of correct data.
分 类 号:P423.3[天文地球—大气科学及气象学]
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