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首页> 外文期刊>Climatic Change >Knowledge extraction from large climatological data sets using a genome-wide analysis approach: application to the 2005 and 2010 Amazon droughts
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Knowledge extraction from large climatological data sets using a genome-wide analysis approach: application to the 2005 and 2010 Amazon droughts

机译：使用全基因组分析方法从大型气候数据集中提取知识：应用于2005年和2010年亚马逊干旱

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摘要

Today, the volume of data generated in almost all disciplines, particularly in meteorology and climate science, is dramatically increasing. Among the challenges generated by this "data deluge" is the development of efficient knowledge discovery strategies. Here, we show that statistical and computational tools used to analyze large data sets of genome-wide studies can be fruitfully applied to a climatic context. Although not as powerful as some techniques already in use by climatologists, these tools are simple and robust, and can easily be adapted to detect early warning signals for extreme events like droughts or be used to filter large data sets before applying other more advanced and computationally expensive methods. We test this approach in our investigation of the causes of the Amazon droughts of 2005 and 2010. Our results highlight the major role played in these extreme events by the warming of the sea's surface temperature, mainly in the tropical North Atlantic. Our findings are in agreement with several analyses published in the literature. The main message we convey is that free and open-source data mining and visualization techniques routinely used in genetic studies can be useful in helping scientists to extract knowledge from large climatic data sets, particularly in regions of the world that are vulnerable to climate change but where the availability of technical expertise is critically scarce.
机译：如今，几乎所有学科，特别是气象学和气候科学学科中生成的数据量都在急剧增加。这种“数据泛滥”所产生的挑战之一是有效知识发现策略的发展。在这里，我们表明，用于分析全基因组研究的大型数据集的统计和计算工具可以有效地应用于气候环境。尽管这些工具不如气候学家已经使用的某些技术强大，但它们简单而强大，可以轻松地用于检测干旱等极端事件的预警信号，或者在应用其他更高级的计算方法之前用于过滤大型数据集昂贵的方法。我们在调查2005年和2010年亚马逊干旱的原因时测试了这种方法。我们的结果强调了主要由热带北大西洋引起的海表温度升高在这些极端事件中发挥的主要作用。我们的发现与文献中发表的几种分析结果一致。我们传达的主要信息是，基因研究中常规使用的免费开放源数据挖掘和可视化技术可以帮助科学家从大型气候数据集中提取知识，尤其是在世界上易受气候变化影响的地区，那里的技术专长极度匮乏。

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