Search Results
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
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
), 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
), 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
FOWD: A Free Ocean Wave Dataset for Data Mining and Machine Learningmodifier of rogue wave risk, whereas nonlinear effects (at least those governed by steepness and BFI) seem to play a minor corrective role in comparison with that. However, it is important to keep in mind that we are only looking at one set of stations and only one sea state parameter at a time. 6. Conclusions FOWD is a free ocean wave dataset that relates wave point measurements to the conditions in which the wave occurred and that is optimized for use in data-mining and machine-learning applications
modifier of rogue wave risk, whereas nonlinear effects (at least those governed by steepness and BFI) seem to play a minor corrective role in comparison with that. However, it is important to keep in mind that we are only looking at one set of stations and only one sea state parameter at a time. 6. Conclusions FOWD is a free ocean wave dataset that relates wave point measurements to the conditions in which the wave occurred and that is optimized for use in data-mining and machine-learning applications
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
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
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
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
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
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
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
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
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
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
from p a , and red point p d is considered noise. Figure adapted from Fig. 2 of Ester et al. (1996) . b. Two-dimensional TRACLUS technique A two-dimensional trajectory clustering algorithm (TRACLUS; Lee et al. 2007 , hereafter L07 ), is widely used in the data mining field (e.g., Aggarwal 2015 ) but has not yet been applied to atmospheric science. This method meets many of the criteria laid out above. It provides a distance calculation method for determining how different two
from p a , and red point p d is considered noise. Figure adapted from Fig. 2 of Ester et al. (1996) . b. Two-dimensional TRACLUS technique A two-dimensional trajectory clustering algorithm (TRACLUS; Lee et al. 2007 , hereafter L07 ), is widely used in the data mining field (e.g., Aggarwal 2015 ) but has not yet been applied to atmospheric science. This method meets many of the criteria laid out above. It provides a distance calculation method for determining how different two
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
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
