首页> 外文会议>International Joint Conference on Neural Networks;IJCNN 2009 >Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach
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Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach

机译:西班牙每日最高气温结构的气候变化:统计和计算智能相结合的方法

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Two blocks (1904-1921 and 1990-2007) of daily maximum temperature data from seventeen Spanish meteorological stations exhibit a multimodal empirical distribution function (EDF). Most of the stations show important differences in their EDF for each one of the considered periods of time, a fact that reveals the complexity of climatic changes within the accepted general warming trend of the Iberian Peninsula. As a tentative approach to understand the underlying structure of data, each EDF has been decomposed on two normal distributed functions. The parameters describing these functions for each station and for each time period have been space-optimized and visualized using classical optimization and genetic programming. The changes in the geographical distribution of the classes derived from the analysis point towards a recent greater role of Mediterranean climates, spreading its influence to the interior of the Peninsula. The general picture, however, is much more complex than a linear warming and a number of stations even show negative trends. This study is considered to be a preliminary methodological exploration of future procedures destined to close the gap between data driven analysis and what models based upon first principles may tell.
机译:来自西班牙十七个气象站的每日最高温度数据的两个区块(1904-1921和1990-2007)展现出多峰经验分布函数(EDF)。在每个所考虑的时间段中,大多数台站的EDF都显示出重要差异,这一事实揭示了在伊比利亚半岛公认的总体变暖趋势内气候变化的复杂性。作为了解数据底层结构的一种尝试性方法,每个EDF都已分解为两个正态分布函数。使用经典的优化和遗传程序,对每个站和每个时间段描述这些功能的参数进行了空间优化和可视化。从分析得出的类别的地理分布变化表明,地中海气候最近起了更大的作用,将其影响扩展到了半岛内部。但是,总体情况要比线性变暖复杂得多,许多站点甚至显示出负面趋势。这项研究被认为是对未来程序的初步方法论探索,旨在缩小数据驱动分析与基于第一原理的模型之间的差距。

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