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Om P. Tripathi, Mark Baldwin, Andrew Charlton-Perez, Martin Charron, Jacob C. H. Cheung, Stephen D. Eckermann, Edwin Gerber, David R. Jackson, Yuhji Kuroda, Andrea Lang, Justin McLay, Ryo Mizuta, Carolyn Reynolds, Greg Roff, Michael Sigmond, Seok-Woo Son, and Tim Stockdale

the time scale of 16–60 days—associated with improved stratospheric fidelity in NWP models—is conditional. They found that such enhanced tropospheric skills are associated with only those forecasts that are initialized at or close to the onset of SSW events. Efforts to predict SSWs in operational forecasting systems began in the 1980s. Using a series of 10-day forecasts from a general circulation model, Mechoso et al. (1985 , 1986 ) attempted to predict the SSW that occurred in the 1979

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Gregor Robinson, Ian Grooms, and William Kleiber

complicate the Fourier-scale analysis of filtering when observations are not available at every grid point, especially when the observation grid is irregular ( Majda and Harlim 2012 , chapter 7). Recall from the introduction that Snyder et al.’s estimate in (4) of the ensemble size required depends on the system covariance, the observing system, and the observation error covariance. Let us ground the theoretical discussion with general comments about the nature of these quantities in operational

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Allan H. Murphy

evidently were not provided, it is quitelikely that many (most?) of the forecasts were unconditional forecasts, or at least that improper and/orinconsistent account was taken of the conditioningevent. The belief that forecasters frequently do nottake proper account of such considerations whenformulating their forecasts is supported by the resultsof operational and experimental programs involvingsubjective probability forecasts of precipitation occur~rence (e.g., Hughes, 1967; Winkler and Murphy,1976

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F. Harnisch, S. B. Healy, P. Bauer, and S. J. English

for Atmospheric Sounding (GRAS) instrument on Meteorological Operational-A ( MetOp-A ) ( Luntama et al. 2008 ), and various missions of opportunity, such as Gravity Recovery and Climate Experiment-A ( GRACE-A ; Wickert et al. 2009 ), the synthetic aperture radar (SAR) satellite TerraSAR-X ( Beyerle et al. 2011 ), and the Communications/Navigation Outage Forecasting System (C/NOFS), are now routinely assimilated at the operational numerical weather prediction (NWP) centers. They provide a

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Michael S. Fischer, Brian H. Tang, and Kristen L. Corbosiero

1. Introduction The rate of tropical cyclone (TC) intensification is dependent upon the interaction of convective-scale processes with the surrounding synoptic-scale environment. Considering the complexities involved in such multiscale interactions, improvements in TC intensity prediction have been challenging. Although operational TC intensity forecasts have experienced gradual improvements over recent years (e.g., DeMaria et al. 2014 ), TC intensity prediction remains especially challenging

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Naila F. Raboudi, Boujemaa Ait-El-Fquih, and Ibrahim Hoteit

) sequential methods that follow a probabilistic framework to split the Bayesian estimation problem into cycles of alternating forecast steps, to advance the distribution of the system state with the model, and analysis steps, to compute the posterior distribution based on incoming observations ( Todling 1999 ). The ensemble Kalman filter (EnKF) was introduced by Evensen (1994) as a Monte Carlo implementation of the Kalman filter (KF) ( Kalman 1960 ) to tackle large-scale, sequential, nonlinear DA

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C. E. Williford, R. J. Correa-Torres, and T. N. Krishnamurti

enhanced forecasts to operational forecasts for the same storms. 2. Experiment description For these experiments, the European Centre for Medium-Range Weather Forecasts Global Spectral Model analyses (at a resolution of 1.125°) were chosen since these analyses were nearly the same resolution as we used in our forecasts. Also, we wanted to view storm development and test our own vortex inclusion techniques within our model; this ruled out use of the National Centers for Environmental

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Rebecca S. Duell and Matthew S. Van Den Broeke

also influence the results—for instance, some forecast offices aggressively collect reports of severe weather, while others may not (e.g., Smith 1999 ). In total 21 (54%) of the 39 of MRV dryline events were associated with severe weather reports in the state(s) in which the dryline was identified. Just over half of MRV dryline days were associated with regional severe weather outbreaks, likely reflecting the synoptically active nature of these events. The state with an algorithm

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Franco Molteni and T. N. Palmer

scheme was based on statistical relations between skill scores (the predictands) anda number of predictors including consistency between consecutive forecasts, amplitude of ve~j short-rangeforecast errors, and indices of large-scale regime transitions. The results of the experiment are assessed withparticular attention to a period with large variations in the skill of the operational forecast.1. Introduction The practical usefulness of a medium-range weatherforecast would be greatly enhanced if

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Thomas M. Hamill, Jeffrey S. Whitaker, and Xue Wei

) temperature and precipitation tercile probabilities. These are forecasts of the probability that the temperature and precipitation averaged over these periods will be below the 33d or above the 67th percentile of the distribution of climatological observed temperatures and precipitation. Forecasters at CPC synthesize information from the NCEP ensemble prediction system as well as models from other weather services and other statistical tools. As will be shown, the skill of operational week 2 forecasts is

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