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Richard J. Hall, Adam A. Scaife, Edward Hanna, Julie M. Jones, and Robert Erdélyi

). Table 5. Verification statistics for 1980–97 regression model, with the sea ice trend retained and removed, for the training period (1980–97) and the testing period (1998–2015). 5. Discussion While much work has suggested that the variability of the NAO/Arctic Oscillation (AO) is due to internal atmospheric dynamics (e.g., James and James 1989 ; Hurrell et al. 2003 ), analysis with GloSea5 and statistical models indicates that there does appear to be a significant predictable component in the

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Jerome P. Charba, Frederick G. Samplatsky, Andrew J. Kochenash, Phillip E. Shafer, Judy E. Ghirardelli, and Chenjie Huang

study addresses a continuing need for skillful thunderstorm forecast guidance for the user community. The history of automated gridded thunderstorm guidance forecasts dates back to the early 1970s, as the Techniques Development Laboratory [now Meteorological Development Laboratory (MDL)] of the National Weather Service (NWS) used a statistical model with a linear regressions equations framework to estimate very short-range (2–6 h) thunderstorm probabilities ( Charba 1977 ) on an 80-km grid for the

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Toshichika Iizumi, Yonghee Shin, Jaewon Choi, Marijn van der Velde, Luigi Nisini, Wonsik Kim, and Kwang-Hyung Kim

predictors are used as input to build the statistical models that explain the variability in the time series of reported national yield statistics obtained from Eurostat. The statistical methods used to forecast the national yields are trend analysis, regression analysis, and similarity analysis based on principal component analysis and cluster analysis. 2) USDA forecasts Yield forecasts for the United States at the national and state levels for the 2019 season were collected from NASS Quick Stats. For

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Richard J. Krupar III, John L. Schroeder, Douglas A. Smith, Song-Lak Kang, and Sylvie Lorsolo

sensitivity analysis was carried out to examine how the linear regression coefficients varied with respect to each storm at each ASOS site evaluated in this study. The storm-specific linear regression coefficients and fit statistics are shown in Table 11 . In comparison to the WSR-88D site-specific coefficients (excluding KEYW since a second-degree polynomial fit was employed), it is apparent that some storms dominate the site-relative sample. For example, Fay (2008) makes up approximately 82% of the

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Kieran T. Bhatia and David S. Nolan

consensus intensity models (IVCN and S5YY). The predictors used for the forecasts of consensus intensity and track forecast error were obtained solely from the real-time files of the Automated Tropical Cyclone Forecasting System (ATCF; Sampson and Schrader 2000 ). For the prediction of CONU track error, Goerss (2007) showed the regression analysis was able to explain a large portion of the variance of independent data, ranging from 23% to 46% for the 2003 Atlantic hurricane season. However, when

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Daniel T. Eipper, George S. Young, Steven J. Greybush, Seth Saslo, Todd D. Sikora, and Richard D. Clark

the temperature analyses, this possible source of error is recommended as a topic for further examination. 3. Analysis We begin our analysis with an examination of three hypotheses for the inland penetration of LLAP bands and then move on to the development of regression models for InPen. a. Examination of hypotheses for InPen 1) Advection-only hypothesis In the absence of any mechanism for enhancing the inland penetration of LLAP bands, a reasonable hypothesis is that InPen will be proportional

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Kirkwood A. Cloud, Brian J. Reich, Christopher M. Rozoff, Stefano Alessandrini, William E. Lewis, and Luca Delle Monache

, Ghosh and Krishnamurti (2018) illustrated the promising use of a generalized regression neural network in deriving a weighted multimodel consensus forecast for intensity. Another promising approach in the realm of postprocessing is to calibrate current forecasts from a NWP or statistical model using past forecasts from the same model along with accompanying historical observational data. Some examples of these types of postprocessing techniques for a deterministic model or a single-model ensemble

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Scott Applequist, Gregory E. Gahrs, Richard L. Pfeffer, and Xu-Feng Niu

linear regression, discriminant analysis, logistic regression, neural networks, and a classifier system. Generalized additive modeling (GAM) with a smoothing spline, which is a nonparametric regression model, was also tried, but we abandoned it when we found it to be less skillful than a simpler class of GAM, namely logistic regression. We believe that this is due to overfitting of a higher-order relationship on the training data. The plan of this paper is as follows. Section 2 defines the forecast

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Zied Ben Bouallègue

impact of the interaction terms on the results. Section 2 describes the dataset and the verification strategy. Section 3 formalizes the introduction of interaction terms in the extended logistic regression equation. Section 4 presents an analysis of the regression parameters and verification results. We conclude this paper in section 5 . 2. Dataset and verification a. Dataset Developed at the German weather service [Deutscher Wetterdienst (DWD)], COSMO-DE-EPS is an ensemble prediction system

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John Billet, Mark DeLisi, Brian G. Smith, and Cory Gates

variables left out of the study for operational reasons. This unexplained variance would show up as error. This study did not attempt to quantify the error associated with the sources mentioned. Rather, it depended on the results of verification to determine whether the error was manageable enough and whether the techniques employed were robust enough to render useful equations. 3. Predictive equations a. Multiple regression model development and presentation We used S-plus and Statistics Analysis

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