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Ruixin Yang, Jiang Tang, and Donglian Sun

changes is usually based on traditional statistical analysis methods and is used extensively to find associations between TC intensity changes such as intensification or rapid intensification and environmental properties. This statistical analysis can be viewed as a type of “one to one” relation analysis technique. In contrast, in recent years, “multiple to one” data mining techniques have become a widely used approach as they involve extensive (and sometimes exhaustive) searches of hidden

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Richard L. Bankert and Michael Hadjimichael

), the application of quantile regression for probabilistic precipitation forecasts ( Bremnes 2004 ), and turbulence estimations ( Frehlich and Sharman 2004 ). With the work in Hansen and Riordan (2003) being one example, machine learning, data mining, and other artificial intelligence tools are being used more often in the diagnosis and forecasting of meteorological phenomena. Abdel-Aal and Elhadidy (1995) applied a machine-learning modeling tool for forecasting daily maximum temperatures in

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David John Gagne II, Amy McGovern, Jeffrey B. Basara, and Rodger A. Brown

operational forecasters in that area. The resulting data are used to train a complex data-mining algorithm that can assign each storm a probability of tornadogenesis based on what the algorithm deems to be the most important environmental characteristics. The algorithm will be assessed for utility in four ways. First, it will be scored on the skill of its probability forecasts. Second, its selection of relevant attributes will be assessed for physical significance. Third, its skill will be compared with

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Valliappa Lakshmanan and Travis Smith

testing cases. The objective of this paper is to describe a technique that makes it possible to extract features from large amounts of spatial data (typically remotely observed, although it could also be numerical model assimilated or forecast fields) and use the features to answer questions in an automated manner. Such automated analysis based on large datasets is referred to as data mining. Data mining is a multidisciplinary field that provides a number of tools that can be useful in meteorological

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David Ahijevych, James O. Pinto, John K. Williams, and Matthias Steiner

algorithms at classifying radar-based storm type. Another comparative study described by Lakshmanan et al. (2010) found that RF had a slight edge over competing artificial intelligence learning techniques in classifying storm type. Hall et al. (2011) found that the RF was one of the best algorithms in terms of overall skill metrics for short-term clear-sky forecasts, although its underconfidence ( Wilks 2006 , p. 288) made it statistically less reliable than other statistical data mining techniques

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Richard L. Bankert, Michael Hadjimichael, Arunas P. Kuciauskas, William T. Thompson, and Kim Richardson

representing the physics implicit in the data are empirically discovered. We hypothesize that these relationships approximate the physical laws and allow development of the required application. Supervised machine-learning techniques are used to discover patterns in data and to develop associated classification and parameter estimation algorithms. These data-mining methods, used in a Knowledge Discovery from Databases (KDD) procedure, are applied to the cloud-ceiling-height assessment problem. Within the

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Tsegaye Tadesse, Donald A. Wilhite, Michael J. Hayes, Sherri K. Harms, and Steve Goddard

relationships between drought and oceanic and climatic indices in Nebraska using time series data-mining algorithms. Among the factors that determine droughts are atmospheric phenomena, such as the atmospheric circulation, and their relationship with ocean dynamics. Based on such relationships, it is important to consider the impacts of the variability of the oceanic parameters while monitoring drought. Generally, the variability of oceanic parameters is relatively slower than the variability of atmospheric

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Wei Zhang, Yee Leung, and Johnny C. L. Chan

recurvature and landfall, they generally fall short in capturing the intricacies of the underlying mechanisms of such movements ( Krishnamurti et al. 1992 ; Holland and Wang 1995 ; Li and Chan 1999 ; Davis et al. 2008 ). Guided by meteorological knowledge, we, however, might be able to unravel these mechanisms from historical TC tracks via data-mining methods. Regularities thus uncovered can in turn enhance our understanding of TC movements. Therefore, in this two-part series of papers, we will employ

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Wei Zhang, Yee Leung, and Johnny C. L. Chan

landfall along the Chinese coast when the steering current is persistently strong and westward. By contrast, westward-moving TCs will turn to the north and then to the northeast if the steering flow is weakened or even changes direction from westward to eastward as a result of the interaction of large-scale circulation. In general, TCs will move far away from the Chinese coast without making landfall there because of the reversal of the steering flow from westward to eastward. Data mining is the

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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

optical sensor observations for an operational finescale SMAP SM product, this study intercompares algorithms introduced in recent literature using in situ SM measurements. Three downscaling algorithms are introduced including 1) a linear regression algorithm using surface vegetation and temperature observations ( Fang et al. 2013 ), 2) a data mining technique (regression tree), using visible and thermal data ( Gao et al. 2012 ), and 3) enhancement of brightness temperature using oversampling of

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