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Mark L. Morrissey, Howard J. Diamond, Michael J. McPhaden, H. Paul Freitag, and J. Scott Greene

). In addition, rain gauges are also deployed in the Atlantic on the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoy network ( Bourlès et al. 2008 ) and in the Indian Ocean as part of the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA; McPhaden et al. 2009 ). These buoy-mounted rain gauges ( Serra et al. 2001 , hereafter S01 ) are R. M. Young self-siphoning, capacitance-type gauges and are mounted 3.5 m above the ocean surface

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Huade Guan, Xinping Zhang, Oleg Makhnin, and Zhian Sun

) and effective temperature estimated from plant distribution datasets in mountain terrains ( Reger et al. 2011 ). The objectives of this study are to 1) examine an algorithm for temperature mapping which incorporates transformed terrain aspect and slope into a multiple linear regression for mean monthly T max and T min mapping, 2) explore the physical mechanisms behind what is inferred from the regression analysis, 3) compare the performance of regression-based algorithms with a selected local

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Timothy DelSole

distribution is not a probability distribution in the sense that it can be estimated by repeated trials of an experiment, but rather in the sense that it quantifies our “degree of belief” in the value of β prior to data analysis ( Jaynes 2003 ). Although prior assumptions are not always stated explicitly, they often exists nonetheless. For instance, one manifestation of overfitting is that the regression parameters β 1 , β 2 , . . . , β K vary by orders of magnitude, yet give excellent in-sample fits

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David A. Unger, Huug van den Dool, Edward O’Lenic, and Dan Collins

. Regression relationships a. Simple linear regression Regression has been applied to the output from dynamic numerical prediction models for over 40 yr ( Glahn and Lowry 1972 ; Glahn et al. 2009 ). Regression analysis usually begins with a tentative assumption of a linear relationship between the predictors (in this case the forecasts from a numerical model) and the predictand (observations), with errors represented by the term, ε. For reasons that will become clear later, this will be illustrated by the

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James S. Goerss

invaluable assistance in graphical display and regression analysis and to Jim Gross of NHC and Buck Sampson of NRL Monterey for making possible the implementation of this research on the ATCF. This research was performed on Project A8R2WRP entitled “Quantifying Tropical Cyclone Track Forecast Uncertainty and Improving Extended-range Tropical Cyclone Track Forecasts Using an Ensemble of Dynamical Models,” funded by the National Oceanic and Atmospheric Administration Joint Hurricane Testbed administered by

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Ning Lin, Renzhi Jing, Yuyan Wang, Emmi Yonekura, Jianqing Fan, and Lingzhou Xue

intensification ( Tang and Emanuel 2012 ), we also construct fully nondimensional VI models. The models based on VI, as well as on its component variables, are compared with the models based on previously considered environmental variables. Recent advancement in statistical analysis of complex, large datasets also motivates us to explore new regression methods applied to TC intensity modeling. First, similar to Lee et al. (2015) , we reduce the SHIPS and STIPS models by statistically identifying the most

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Andreas Muhlbauer, Peter Spichtinger, and Ulrike Lohmann

1. Introduction Within the framework of trend analysis, simple linear least squares (LS) regression models are widely used and allow for an extrapolation of different atmospheric variables into the future (e.g., Born 1996 ; Dai et al. 1997 ; Zerefos et al. 2003 ; Norris 2005 ; Solomon et al. 2007 ). Although linear regression models have been used successfully, a number of difficulties arise with the conceptual framework of linear trend analysis and its applicability to problems of

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Klaus Dolling, Elizabeth A. Ritchie, and J. Scott Tyo

signal as the DAV signal fluctuates more in the first few hours, until the filter is properly established. Using this method, smoothed spatiotemporal maps are produced for each of the 21 TCs. Finally, information from the CIRA extended best-track file, which provides the same quadrant wind radii information as the NHC best-track archive (post 2004), and the SHIPS model is used to conduct the regression analysis. For the purposes of this study, the symmetric wind radii are calculated by averaging the

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Caren Marzban, Xiaochuan Du, Scott Sandgathe, James D. Doyle, Yi Jin, and Nicholas C. Lederer

manner in which MMR allows one to account for spatial correlations ( DelSole and Yang 2011 ). Although other methods exist that take spatial correlations into account when performing inference ( Douglas et al. 2000 ; Elmore et al. 2006 ; Wilks 1997 ), the MMR approach is more natural in the present application because the sensitivity analysis is done within a regression framework already. The terms “multivariate” and “multiple” in MMR refer to several response and predictor variables, respectively

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Martin P. Tingley and Bo Li

the latter treated as the independent variable. LOC makes the additional assumption that the instrumental series are free of observational errors. In terms of inference tools , the parameters α i and λ i are inferred using ordinary least squares regression over a calibration interval. The resulting maximum likelihood estimates of the parameters are then used in the second stage of the analysis to impute past temperatures via [cf. Eq. (A6) from C11 ] In the third and final stage of the

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