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Minghua Zheng, Edmund K. M. Chang, and Brian A. Colle

cyclone or precipitation band in an ensemble, or why two adjacent forecast cycles are inconsistent in forecasting these phenomena. In this paper, an ensemble sensitivity method is employed to link the ensemble forecast uncertainties with the initial conditions, and thus it provides guidance for adaptive observing strategies as well as interpreting daily ensemble runs ( Ancell and Hakim 2007 ; Torn and Hakim 2008 , 2009 ). Ensemble sensitivity analysis employs a linear correlation and regression

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Gregory J. Connor and Frank Woodcock

knowledge of a numerical model performance characteristics within a synoptic regime, that enable the adding of value to NWP-based guidance on some occasions ( Funk 1991 ; Junker et al. 1992 ; Verret et al. 1996 ). Tree-based statistical models are an emerging group of data analysis techniques employing multiple discriminant analysis and a hierarchical set of binary decision nodes. Classification and regression trees ( Breiman et al. 1984 ), fast algorithm for classification tress ( Loh and

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Ira S. Brenner

forecasts were calculated from regression equations that hadbeen derived from model output statistics (MOS). During the analysis period, from October 1982 throughSeptember 1984, si?ificant cold biases, of ~ 1.5-2.5-F (0.8-1.4-C), were determined for the MOS minimumtemperature forecasts at 24, 36 and 48 h. The maximum temperature forecasts had warm biases < 1.0-F (0.6-C)that were significant only at 24 h. The minimum and maximum temperatures from the most recent 30-yearnormals (1951-80) were

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Ronald M. Reap

time of maximum solar insolation. A relatively high incidence ofpositive flashes was found during all phases of thunderstorm occurrence. High flash accumulations over mountainous regions clearly revealed the affinity of lightning activity for elevated terrain. Maximum activity wasfound to occur earliest in the higher elevations, moving to lower elevations later in the day. Linear screening regression analysis and the Model Output Statistics approach were used to statistically relatelightning

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Kevin T. Law and Jay S. Hobgood

purpose of this paper is to present an alternative method to a statistical 24-h intensity model. The premise of this study is that different statistical models must be applied for different intensities of hurricanes and at different stages during the life cycle of the hurricane. Rather than using one regression model for a particular forecast interval, the model presented here uses a discriminant function analysis that selects from a collection of 24-h regression models, which is used to predict both

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Michael K. Tippett, Mansour Almazroui, and In-Sik Kang

variability on intraseasonal time scales includes the equatorial region from ~80° to 110°E ( Hoell et al. 2012 ). Observational analysis shows that enhanced convection in this region on intraseasonal time scales is associated with suppressed convection over much of the AP region and an upper-level anticyclone centered across South Asia. Analysis of the thermodynamic balance shows cold temperature advection in the AP region to be mostly balanced by downward motion consistent with dry conditions. This

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Steven J. Greybush, Sue Ellen Haupt, and George S. Young

forecast date as belonging to a specific regime as is done in the clustering approach. Because it uses linear regression of other atmospheric variables to postprocess model output, this technique is somewhat analogous to traditional MOS ( Glahn and Lowry 1972 ). It offers an advantage over MOS, however, in that it considers atmospheric data from the entire domain rather than just at the point location. Moreover, these data have been compressed through the principal component analysis, thus reducing the

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Ira S. Brenner

.e., areal coverage) and 2) the daily average rainfall amount (including 0) reported for all 29 stations correlated with each of the selected parameters. The third goal was addressed by performing multiple linear regression analysis on the database to produce a single prediction equation each for average areal coverage and rainfall amount. The two equations were tested on an independent dataset to determine viability as a reliable objective forecast aid to predict average areal coverage and rainfall

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Michael C. Coniglio, Harold E. Brooks, Steven J. Weiss, and Stephen F. Corfidi

soundings in a discriminant analysis. As detailed later, we find that the percentage of correct groupings converges to 1%–2% after the inclusion of only four variables in the analyses, which is a reflection of the substantial mutual correlations among the variables. c. Logistic regression Once the best predictors are identified, logistic regression 3 is used to develop an equation that gives the probability of one of the two groups occurring. Logistic regression is a method of producing probability

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John A. Knaff and Robert T. DeMaria

sample. 6. Summary and discussion This manuscript describes the development of an IR eye probability forecast scheme. The scheme was developed using an eye detection algorithm that employed a linear discriminant analysis technique to determine the probability of an eye existing in any given IR image given information about the storm center, motion, and latitude. The methodology used is logistic regression, where predictors were selected from routine information about the current storm, forecast

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