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Pablo Rozas-Larraondo, Iñaki Inza, and Jose A. Lozano

paper, a simple form of kernel nonparametric regression is used to improve forecasts of wind speed coming from the NWP, considering wind direction and wind speed variables to filter out data. This form of regression is particularly suitable for real-time forecasting, because it can be updated to include the most recent data, which makes it a perfect candidate for operational on-demand applications. Wind statistical analysis cannot be performed directly by applying out-of-the-box machine learning or

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T. Ghosh and T. N. Krishnamurti

1. Introduction Consensus forecasts for meteorological events were operationally used in the pioneering studies of Toth and Kalnay (1993 1997 ), Molteni et al. (1996) , Houtekamer et al. (1996) , and Goerss (2000) . Krishnamurti et al. (1999) introduced the notion of a multimodel superensemble (MMSE) to combine multimodel forecast datasets using a linear multiple regression approach that utilized the mean-square error reduction principle. Studies reported on the efficiency of this

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Edward N. Rappaport, James L. Franklin, Andrea B. Schumacher, Mark DeMaria, Lynn K. Shay, and Ethan J. Gibney

the basin-wide regression lines, and the large scatter (low r ) about the basin-wide lines. We also performed an analysis of the central pressure because it is sometimes considered a proxy for tropical cyclone intensity and can be measured independently from wind speed. Central pressure changes (not shown) were consistent with, and correlations were similar to, the findings for wind speed for the respective tropical depression–tropical storm and hurricane subgroups. It is interesting that the

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Daniel Gombos and Ross N. Hoffman

incoming observations in advance of relatively slow data assimilations ( Gombos 2009 ), and 3) identify, for purposes including supplementary forecast guidance and adaptive observing, the antecedent atmospheric features to which the ExDS is most sensitive ( Gombos et al. 2012 ). Ensemble regression is a multivariate linear inverse technique that uses ensemble model output to make inferences about the linear relationships between vector-valued forecast and/or analysis fields, often gridded “maps” of

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Yulia R. Gel

temporally weighted spatial composition of recent observations over a “neighborhood” of weather observing sites while taking into account land use categorization. The CART–ACE method is based on the spatiotemporal analysis of bias using modern statistical nonparametric regression techniques such as alternative conditional expectation (ACE) and regression trees. Both approaches can be applied to any site of interest without any history of bias measurement at that particular site. Although in general the

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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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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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Klaus Dolling, Elizabeth A. Ritchie, and J. Scott Tyo

signal as the DAV signal fluctuates more in the first few hours, until the filter is properly established. Using this method, smoothed spatiotemporal maps are produced for each of the 21 TCs. Finally, information from the CIRA extended best-track file, which provides the same quadrant wind radii information as the NHC best-track archive (post 2004), and the SHIPS model is used to conduct the regression analysis. For the purposes of this study, the symmetric wind radii are calculated by averaging the

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