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Kelsey M. Malloy and Ben P. Kirtman

agreement between ensemble mean and observation. d. Predictor analysis By comparing the large-scale teleconnection patterns of CCSM4 forecasts and MERRA-2, a consistent link between the standardized index and V900 in the model provides an opportunity for forecasters. Linear regressions determine “slope” associations between variables and the index time series. It may also help explain discrepancies in atmospheric response between the model and reanalysis. The results reflect the mean from each ensemble

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Kelsey M. Malloy and Ben P. Kirtman

agreement between ensemble mean and observation. d. Predictor analysis By comparing the large-scale teleconnection patterns of CCSM4 forecasts and MERRA-2, a consistent link between the standardized index and V900 in the model provides an opportunity for forecasters. Linear regressions determine “slope” associations between variables and the index time series. It may also help explain discrepancies in atmospheric response between the model and reanalysis. The results reflect the mean from each ensemble

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Andrew Devanas and Lydia Stefanova

climatology. 5. Summary and discussion More than 200 separate variables were extracted from 1200 UTC Key West soundings for the period 2006–14 for the wet-season months from June through September. The variables were separated into two subsets: days when at least one waterspout was reported and days with no waterspout reports. The number of variables considered needed to be reduced in order to obtain a workable number of candidate predictors for logistic regression analysis. Each variable was examined for

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Zhi-Ping Yu, Pao-Shin Chu, and Thomas Schroeder

,q U ′( E s,i )′ X s,t . (9) d. Principal Component Regression (PCR) model In the CCA model, we assumed that the predictor field is a matrix of known constants. If that matrix is random, then the analysis is carried out conditional on it so that it is still treated as if it were fixed. The initial EOF truncations also cause a problem. In CCA, it is implicitly assumed that the first four modes of the predictand field are the ones that are most highly correlated with the first eight modes of the

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Marlis Hofer, Johanna Nemec, Nicolas J. Cullen, and Markus Weber

links each model state to an observed state). The ultimate goal of this analysis is to extend short-term, high-resolution time series from glacierized mountain environments into the past in order to provide a well-grounded knowledge base for estimating future changes. Section 2 provides a general literature overview on predictor selection in SD. Sections 3 and 4 describe the observational and reanalysis datasets used in this study. Section 5 gives an overview about “sDoG,” the regression

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Yiwen Mao and Adam Monahan

, we will develop a descriptive model aiming at clarifying the relationship between wind predictability and statistical properties (i.e., standard deviation and kurtosis) of wind component fluctuations in an idealized conceptual framework. Note that statistical predictability of wind components is not directly related to the mean vector wind, as the regression analysis considers fluctuations around the mean. It follows that the apparent relationship between the orientation of the mean wind and

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Colin Gallagher, Robert Lund, and Michael Robbins

in favor of should conclusions conflict for reasons illustrated in the next section. It remains to derive results 3 and 4, which is done in the appendix. This task is important since the asymptotic distribution depends on the presence/absence of a trend. One would have to perform yet another derivation for regression structures employing sinusoidal, quadratic, or exponential terms. Sinusoidal terms, for example, could arise in the analysis of daily or monthly series. This said, the derivations

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Atoossa Bakhshaii and Roland Stull

subroutines for buoyancy, convection, turbulence, cloud microphysics, and precipitation. Hence, NWP forecasts that finish sooner can provide earlier warnings of severe storms, heavy precipitation, wind and solar inputs to clean energy production, and other weather elements produced by NWP. Section 2 examines differences among a few published thermodynamic diagrams and describes which data will be used for this study. Section 3 explains the methodology of symbolic regression used to find predictands

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Qinglan Li, Zenglu Li, Yulong Peng, Xiaoxue Wang, Lei Li, Hongping Lan, Shengzhong Feng, Liqun Sun, Guangxin Li, and Xiaolin Wei

of TC intensity prediction. Dvorak (1975) provided a technique, which contained some subjectivity, to estimate TC intensity over open oceanic areas by using satellite images. Jarvinen and Neumann (1979) proposed statistical regression equations for the prediction of TC intensity change out to 72 h over the North Atlantic basin by using predictors derived from climatology and persistence (SHIFOR). It is believed by meteorologists that the environment affects the intensity change of TCs. Pike

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Debbie Polson, Gabriele C. Hegerl, Xuebin Zhang, and Timothy J. Osborn

and masked to match the spatial and temporal data availability of that dataset on a grid-box basis. The models used in this analysis are listed in Table 1 . Table 1. List of modeling groups and models [including expansions and institute identification (ID)] used in this analysis. 4. Zonal patterns of precipitation change Zonal-mean change in precipitation is calculated by applying a linear least squares regression to precipitation averaged across each zonal band and is expressed as the

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