Application of spatiotemporal pattern recognition techniques on predicting extratropical transition (and reintensification) of tropical cyclones
Traditional spatial decompositions such as application of Empirical Orthogonal Functions and Principal Components Analysis (PCA) of meteorological field data have received wide attention in numerous forecasting and analysis applications. The method has been used to identify the important spatial patterns in meteorological fields. Defining the variability between various cases of tropical cyclones is another application of the method. We are utilizing spatial and temporal data as a tool to differentiate the TCs that complete and fail to complete extratropical transition in the western North Pacific. It has been shown that EOF Analysis is a promising tool in the prediction of reintensification of TCs after ET
REVIEW ARTICLE ROTATION OF PRINCIPAL COMPONENTS
Recent research has pointed to a number of inherent disadvantages of unrotated principal components and empirical orthogonal functions when these techniques are used to depict individual modes of variation of data matrices in exploratory analyses. The various pitfalls are outlined and illustrated with an alternative method introduced to minimize these problems via available linear transformations known as simple structure rotations. The rationale and theory behind simple structure rotation and Procrustes target rotation is examined in the context of meteorological/climatological applications. This includes a discussion of the six unique ways to decompose a rotated data set in order to maximize the physical interpretability of the rotated results. The various analytic simple structure rotations available are compared by a Monte Carlo simulation, which is a modification of a similar technique developed by Tucker (1983), revealing that the DAPPFR and Promax k = 2 rotations are the most accurate in recovering the input structure of the modes of variation over a wide range of conditions. Additionally, these results allow the investigator the opportunity to check the accuracy of the unrotated or rotated solution for specific types of data. This is important because, in the past, the decision of whether or not to apply a specific rotation has been a ‘blind decision’. In response to this, a methodology is presented herein by which the researcher can assess the degree of simple structure embedded within any meteorological data set and then apply known information about the data to the Monte Carlo results to optimize the likelihood of achieving physically meaningful results from a principal component analysis.
A component analytic approach to precipitation regionalization in California
SummaryFour precipitation regions in California are identified and described using P-mode principal components analysis. A regionalization of regimes was accomplished by classifying data points (precipitation stations) with respect to which component they loaded highest on. Three primary regions were found to exist and their spatial extent is presented in map form. Data for study were 120 monthly precipitation totals observed from 1961–1970 at each of 90 randomly selected stations within California.ZusammenfassungVier Niederschlagsregionen in Kalifornien werden unter Anwendung der Analyse von Hauptkomponenten identifiziert und beschrieben. Datenpunkte (Niederschlaggstationen) wurden je nach dem Hauptanteil der Komponenten klassifiziert. Es konnten dadurch drei primäre Niederschlagsregionen festgestellt werden, deren Ausdehnung kartenmäßig dargestellt wird. Als Daten für diese Studie dienten 120 monatliche Niederschlagssummen zwischen 1961 und 1970 von insgesamt 90 willkürlich gewählten kalifornischen Stationen.