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Chiara Lepore and Michael K. Tippett

° grid over the contiguous United States (CONUS) and to monthly resolution over the period 1979–2016. Report data are analyzed separately for tornadoes rated EF0 (23 458 cases), EF1 (12 799 cases), EF2 (4015 cases), and EF3 (1141 cases). Tornadoes rated EF4 and greater represent less than 1% of the tornadoes in the database and are not included in the analysis here. Following Tippett et al. (2012) , the TEI-EF is developed using environments from the North American Regional Reanalysis ( Mesinger et

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R. Mínguez, B. G. Reguero, A. Luceño, and F. J. Méndez

more than one function of X in the regression Eqs. (1) or (22) . Consequently, we have investigated some of these more complex models, but we will only show results for those models we have found to work best. Before performing the analysis, the particular regression models we have chosen are presented: For the WLS method ( section 3a ), the response variable is transformed using Eq. (19) and the estimate is calculated based on Eq. (20) . Because the relationship between X and Y is

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Hamish A. Ramsay, Michael B. Richman, and Lance M. Leslie

sample is used once as the testing data. In the present analysis, LOOCV uses 34 years of paired SST and TCC data (defined as “training data”) to build the regression model coefficients and applies those coefficients to predict the year withheld (defined as “testing data”). The LOOCV approach generates 35 sets of model coefficients and performance indices for evaluation. LOOCV assumes that there is no significant autocorrelation at lag 1 ( Wilks 2011 ). This assumption was tested, and the lag-1

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Pierre G. F. Gérard-Marchant, David E. Stooksbury, and Lynne Seymour

regression using repeated medians. Biometrika , 69 , 242 – 244 . Siegel , S. , and N. Castellan , 1988 : Nonparametric Statistics for the Behavioural Sciences . McGraw Hill, 399 pp . Theil , H. , 1950 : A rank invariant method of linear and polynomial regression analysis, (parts 13). Ned. Akad. Wet. Ser. A , 53 , 386 – 392 . 521 – 525 . 1397 – 1412 . Zhao , X. , and P. S. Chu , 2006 : Bayesian multiple changepoint analysis of hurricane activity in the Eastern North Pacific: A

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Anthony G. Barnston, Michael K. Tippett, Huug M. van den Dool, and David A. Unger

and his set of constructive suggestions, and also the anonymous reviewers who provided helpful comments and criticism. This study was supported by NOAA’s Climate Program Office’s Modeling, Analysis, Predictions, and Projections Program Award NA12OAR4310082. Most of the authors participated in NOAA’s Climate Prediction Task Force. REFERENCES Barnston , A. G. , and H. M. van den Dool , 1993 : A degeneracy in cross-validated skill in regression-based forecasts . J. Climate , 6 , 963 – 977

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Sid-Ahmed Boukabara, Kayo Ide, Narges Shahroudi, Yan Zhou, Tong Zhu, Ruifang Li, Lidia Cucurull, Robert Atlas, Sean P. F. Casey, and Ross N. Hoffman

1. Introduction Observing system experiments (OSEs) are data assimilation (DA) and forecast experiments that measure the impact of an observing system by comparing analysis and forecast results with and without the particular observing system. As described by Boukabara et al. (2016) , observing system simulation experiments (OSSEs) extend the concept of OSEs to proposed future sensors by using observations simulated from the nature run (NR). OSSEs support (i) decision-makers by providing

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Daniel Broman, Balaji Rajagopalan, and Thomas Hopson

robust, as studies by Besancenot et al. (1997) in Benin and Yaka et al. (2008) in Niger and Burkina Faso reached similar conclusions. This link between relative humidity and its predictive capability of meningitis risk is corroborated in preliminary analysis ( Pandya et al. 2014 ), shown in Fig. 1 . This figure indicates an inverse relationship between relative humidity and meningitis risk. The probability of exceedance is based on the mean relative humidity for the preceding 4 weeks at a 2-week

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Peter Hitchcock, Theodore G. Shepherd, and Shigeo Yoden

year to year in this region and season ( Fig. 7a ), presumably as a result of the dynamical variability associated with the timing of the final warming. Although we have not demonstrated what role these nonlocal rates play in the break down of the vortex in this model, this result provides a caution against using a local, climatological radiative damping rate in mechanistic modeling studies of this region. 4. Shortwave relaxation rates A similar regression analysis can be performed on shortwave

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Xiao-yong Zhuge, Fan Yu, and Ye Wang

method for the cloud objects (the albedo of more than 0.3) based on the previous work. Because the radiation of ground objects can be strongly affected by aerosols located in the lower atmosphere, for which normalization is difficult because of variations in aerosol phase function, the scope of our method is limited to the normalization with albedos greater than 0.3. In this paper, section 2 introduces the model and data. Section 3 describes the algorithm. The analysis of application scope is

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Johannes P. Werner, Juerg Luterbacher, and Jason E. Smerdon

; Christiansen et al. 2009 ; Tingley et al. 2012 ; Smerdon et al. 2011 ; Wahl and Smerdon 2012 ). The answers to these questions are ultimately fundamental to successful reconstructions of past climatic variability (e.g., North et al. 2006 ; Jansen et al. 2007 ; Jones et al. 2009 ). To address the existing challenges and improve CE climate field reconstructions, multiple methodological approaches have been emerging recently as alternatives to the more traditional multivariate linear regression schemes

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