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Michael K. Tippett, Suzana J. Camargo, and Adam H. Sobel

. Therefore, the choice of level or levels to use as predictors in an index is somewhat arbitrary. Since microwave satellite retrievals of column-integrated water vapor are available, it seems reasonable to consider this quantity as a predictor in place of reanalysis products. Given the possible biases in either satellite retrievals or reanalysis products, it should be noted that the regression can implicitly correct systematic errors in its inputs. In this study, we address the first three limitations

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Jacob Coburn

process. In a study on boundary layer winds in Minnesota, Klink (2007) used the meridional pressure gradient from reanalyzed pressure data as an index by which to remove seasonal synoptic variations before regressing against teleconnection indices. For this analysis, synoptic variations were characterized by both the spatial synoptic pattern and the pressure gradient of those patterns. Many methods of synoptic typing have been derived and used in environmental analysis ( Ramos et al. 2014 ; Huth et

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Shin-Hoo Kang, Tae-Young Goo, and Mi-Lim Ou

essential to increase the accuracy of an initial (or first) guess profile as a proper constraint. To construct a physically reasonable initial guess profile, we used the original statistical regression ( Feltz et al. 2007 ), the KLAPS analysis data, and automated weather station (AWS) data. Before the regression, the original static bias spectrum ( Feltz et al. 2007 ) was subtracted from observed spectra. Then, original regressions were made with the regression coefficients ( Feltz et al. 2007 ), and

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Jeffrey J. Waters, Jenni L. Evans, and Chris E. Forest

(MDR) and a reduced set is retained. Variability is defined in terms of daily anomalies against reference averaging time periods of 10 and 15 days; for the purposes of brevity, we report on the 15-day results here. 1 This base time period is chosen to capture the evolution of active and inactive TCG periods of around 2–3 weeks observed throughout the hurricane season (e.g., Gray 1988 ). By applying principal component analysis (PCA), the variables are transformed into an uncorrelated set of

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Aneesh Goly, Ramesh S. V. Teegavarapu, and Arpita Mondal

variable. Thus, MLR is used to predict the values of a hydrologic variable Y given a set of p predictor variables ( ). Regression analysis is performed using principal components and membership values obtained from the fuzzy c -means clustering method. The following equations involving the seasonal components are used for regression analysis ( Ghosh and Mujumdar 2006 ): where pc is the principal components; μ represents the membership values in each cluster; t is the serial number of the data

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Yunyan Zhang, Bjorn Stevens, Brian Medeiros, and Michael Ghil

) ( Rossow and Schiffer 1999 ). In our analysis the correlation between low-cloud fraction and lower-tropospheric stability is not as strong, and the slope of the regression is somewhat weaker: 5% cloud fraction per kelvin in our case, as compared to 6% per kelvin reported by KH93 . Differences may have a number of origins: (i) low-cloud fraction is measured differently by ISCCP than it was by KH93 , who used the cloud climatology derived from the surface observer network; (ii) the ISCCP low

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Chris T. Jones, Todd D. Sikora, Paris W. Vachon, and John Wolfe

et al. (2006) . Therein, the classification of features in overland SAR images begins with binomial classification using logistic regression. Features are classified as being either 1) forests or hedges or 2) other. A multinomial classifier is subsequently applied to features that fall into the second of these classes. In a similar way, we aimed to classify features in over-ocean SAR images as being either 1) SST front signatures or 2) other. The subjects of our analysis are candidate SST front

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Edward N. Rappaport, James L. Franklin, Andrea B. Schumacher, Mark DeMaria, Lynn K. Shay, and Ethan J. Gibney

the basin-wide regression lines, and the large scatter (low r ) about the basin-wide lines. We also performed an analysis of the central pressure because it is sometimes considered a proxy for tropical cyclone intensity and can be measured independently from wind speed. Central pressure changes (not shown) were consistent with, and correlations were similar to, the findings for wind speed for the respective tropical depression–tropical storm and hurricane subgroups. It is interesting that the

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Filipe Aires

; only the creation of statistically robust impact models able to perform reliable forecasts will be analyzed. Impact models are generally based on a statistical regression ( Sultan et al. 2009 ). The fitting of the models uses a data record with the meteorological variables (plus any available ancillary or a priori information) as inputs and the socioeconomic variable as output. A first difficulty is nonstationarity: if a trend exists for one of the considered variables, it then becomes difficult to

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Chukwuma Otum Ume, Ogochukwu Onah, Kehinde Paul Adeosun, Onyekwe Chris Nnamdi, Nice Nneoma Ihedioha, Chukwuemeka Onyia, and Ezinne Orie Idika

social structures such as internal practices within a particular field, and individual experiences. The objective of understanding the underlying drivers and households’ perception of climate change adaptation fall within this scope of analysis and as such justifies its usage in the study. (A sample of the interview transcript can be found in appendix A , and a detailed result of the regression analysis using the StataCorp Stata, release 14 (Stata 14), software program can be found in appendix B

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