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Machine Learning and Data Mining Approaches to Climate by Valliappa Lakshmanan, Eric Gilleland, Amy McGovern, Martin

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By Valliappa Lakshmanan, Eric Gilleland, Amy McGovern, Martin Tingley

This booklet offers leading edge paintings in weather Informatics, a brand new box that displays the applying of information mining ways to weather technology, and indicates the place this new and quick turning out to be box is headed. Given its interdisciplinary nature, weather Informatics deals insights, instruments and techniques which are more and more wanted which will comprehend the weather approach, a facet which in flip has develop into an important as a result hazard of weather switch. there was a veritable explosion within the volume of knowledge produced by means of satellites, environmental sensors and weather types that video display, degree and forecast the earth method. so that it will meaningfully pursue wisdom discovery at the foundation of such voluminous and numerous datasets, it is vital to use computing device studying tools, and weather Informatics lies on the intersection of desktop studying and weather technology. This e-book grew out of the fourth workshop on weather Informatics held in Boulder, Colorado in Sep. 2014.

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By doing so we do not only reduce the dimensionality of the network, but we more importantly constructed a climate network that is reduced to its teleconnections. We will refer to these networks as teleconnection climate networks (TCN). 2 Method In order to group time series by similarity, we use the standard fast greedy hierarchical agglomerative complete linkage clustering (Defays 1977). This clustering is performed in a metric space with dissimilarities between time series as distances. In this study we focus on the Spearman’s rho correlation coefficient as the similarity measure in order to capture not only linear but also other monotonic relationships and in order to avoid problems of skewed distributions in precipitation data.

Here, we study longrange interrelations within the precipitation system as well as between precipitation and sea surface temperature (SST) dynamics. Our aim is to shed light on the spatial structure of such teleconnections, with a special focus on precipitation dipoles and how they are influenced by SST variability. For this purpose, we employ the climate network approach by representing the interrelations between climatic time series as complex networks (Boers et al. 2013, 2014; Donges et al. 2009a,b; Ebert-Uphoff and Deng 2012; Malik et al.

By considering water as the positive set and land as the negative set, we can evaluate algorithms using the F1 -measure (Pang-Ning et al. 2006) on the dates when LSFRACTION is available. Baseline methods: Below, we introduce the three baseline methods used in our evaluation: K-MEANS, EM, and NCUT. EM (McLachlan and Krishnan 2007) and K-MEANS (MacQueen 1967) group data into multiple clusters such that data within the same cluster have similar feature values, while feature values between different clusters are different.

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