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Savin S. Chand and Kevin J. E. Walsh

standard multiple regression technique. Other studies, for example HH03 , PL86 , and S09 , used a linear discriminant analysis to classify systems into developing and nondeveloping groups through the linear combination of predictor variables. While these ordinary linear regression methods may work well for large sample sizes, their confidence is questionable for small sample sizes. Also, they are appropriate for modeling response data that are quantitative and continuous in nature and therefore may

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Si Gao and Long S. Chiu

equation is derived using TMI OI sea surface temperature data with high temporal and spatial resolutions (daily and ¼°). This equation together with environmental information obtained from NCEP GFS FNL analysis, best tracks taken from RSMC Tokyo, and SLHF and IRR results derived from satellite data are then utilized to develop multiple linear regression models and NN models for western North Pacific TC intensity forecasting at 24-, 48-, and 72-h intervals. Compared to the multiple linear regression

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Jennifer F. Newman, Valliappa Lakshmanan, Pamela L. Heinselman, Michael B. Richman, and Travis M. Smith

radar; however, this is not possible if shear is range dependent. To mitigate this issue, two range-correction methods, a linear regression model and an artificial neural network (ANN), were developed to correct the LLSD shear signatures of simulated circulations. By examining the range dependence of circulations of known strength and size, the true shear of the simulated circulations, calculated using their user-specified diameters and peak velocities, was related to radar-measurable parameters

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Mong-Ming Lu, Pao-Shin Chu, and Yun-Ching Lin

-prone areas in East Asia such as Taiwan ( Chu et al. 2007 ), Korea ( Choi et al. 2009 ), and the East China Sea ( Kim et al. 2010 ). In the meantime, new approaches to predictor selection procedures ( Lee et al. 2007 ; Kwon et al. 2007 ; Fan and Wang 2009 ) were proposed. Recent studies ( Ho et al. 2009 ; Kim et al. 2010 ) have shown that better forecast skill can be achieved by Poisson regression than linear regression when the method was applied to forecasting seasonal TC frequency over the East

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Kyle Davis, Xubin Zeng, and Elizabeth A. Ritchie

that there may be a short lag of a few days between when the data recording finishes and when the required input data are published by the responsible organization. However, this delay in acquiring the input data would cause only a short delay in producing the prediction and is considered inconsequential, as nearly all hurricane activity happens later in the season ( Gray et al. 1993 ). The UA model uses a Poisson regression (i.e., the regression between the logarithm of the number of hurricanes

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Lina Bai, Hui Yu, Peter G. Black, Yinglong Xu, Ming Ying, Jie Tang, and Rong Guo

Meteorological Administration (CMA) during 1949–71 was a set of latitude-dependent regression equations based on in situ observations ( Ying et al. 2014 ). Most recently, a new WPR based on the cyclostrophic wind equation was presented by Holland (2008) . The final form of the WPR was given by (4) MSW = ⁡ ( b s ρ e Δ p ) 0.5 , where Δ p = p env − MSLP, e is the base of natural logarithms, ρ is surface air density, b s = − 4.4 × 10 − 5 Δ p 2 + 0.01 Δ p + 0.03 ⁡ ( ∂ p c / ∂ t ) − 0.014 φ + 0.15 υ t x

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Joseph R. Patton and Henry E. Fuelberg

to terminate a lightning advisory). The GLM methodology is a flexible generalization of ordinary linear regression that allows response variables to have error distributions other than normal. It generalizes linear regression by allowing the model to be related to the response variable via a link function, by permitting the magnitude of the variance of each measurement to be a function of its predicted value, and unifies other statistical models, including linear, logistic, and Poisson regression

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Daniel J. Halperin, Robert E. Hart, Henry E. Fuelberg, and Joshua H. Cossuth

. Separate equations were developed for each global model, basin, and forecast window. Predictors were selected using backward elimination combined with a multiple fractional polynomial analysis. Cross validation was conducted to ensure that the predictor pool was robust. Verification of the regression-based forecasts during the 2014 season revealed that some were well calibrated ( Fig. 2 ). However, it appears that an upgrade to the UKM global model configuration caused the regression equations to

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Huug van den Dool, Emily Becker, Li-Chuan Chen, and Qin Zhang

probabilities to the observed probability. 1 We design a regression method for the second approach. The paper is organized around the formal similarity of regression using variables in physical and probabilistic units, respectively. One of the more prevalent skill metrics for tracking the skill of numerical weather prediction (NWP) is the anomaly correlation (AC). As a notion and as an accepted measure of skill, the AC has been around in the literature since about 1970 ( Miyakoda et al. 1971 ). The word

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

regression model introduced in this paper to that of the CPS analysis in Bieli et al. (2019b , hereafter BCS19) , who examine how well ET storms defined in the CPS agree with those defined in the best track records, using a global set of TCs from 1979 to 2017. Some of the statistics discussed here are not shown explicitly in BCS19 , but were reproduced here by the authors for the purpose of this comparison. In BCS19 , ET onset is defined as the first time a TC is either asymmetric ( B > 11) or has a

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