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Thomas H. A. Frame, John Methven, Nigel M. Roberts, and Helen A. Titley

1. Introduction Ensemble forecasting aims to characterize forecast uncertainty associated with the growth of small uncertainties in the initial conditions of the forecast ( Molteni et al. 1996 ). A natural interpretation of an ensemble forecasting system is as an estimator of the probability of occurrence of future weather events of interest ( Leith 1974 ); for example, in the case of flood prediction, what is the chance of rainfall accumulation exceeding a particular critical threshold value

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Ross N. Bannister

1. Introduction a. Sampling error and localization Progress to improve the efficacy of ensemble data assimilation methods like the ensemble Kalman filter (EnKF) has been impeded by problems with sampling error. Sampling error arises when the number of ensemble members ( N ) is much less than the size of the state vector ( n ) (see e.g., Houtekamer and Mitchell 1998 ; Evensen 2003 ; Lorenc 2003 ). The true forecast error covariance matrix, , can be estimated from an N -member ensemble as

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David M. Schultz and Joseph M. Sienkiewicz

Aeronautics and Space Administration (NASA) SeaWinds microwave scatterometer on the research Quick Scatterometer polar-orbiting satellite (QuikSCAT; e.g., Hoffman and Leidner 2005 ). QuikSCAT was successfully used by marine forecasters to determine extratropical cyclone intensity and wind warning categories for over 10 years from 1999 through November 2009 (e.g., Von Ahn et al. 2005 , 2006 ; Chelton et al. 2006 ). Fig . 1. QuikSCAT imagery of horizontal wind speed and direction at 0730–0733 UTC 8 Dec

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Jeffrey M. Chagnon and Suzanne L. Gray

), diabatic Rossby waves (e.g., Parker and Thorpe 1995 ; Moore and Montgomery 2004 ; Moore et al. 2013 ), and forecast error growth (e.g., Brennan et al. 2008 ; Davies and Didone 2013 ). In the absence of frictional and diabatic processes, PV is materially conserved. Therefore, PV provides a convenient means to characterize the large-scale structure of the atmosphere and its evolution in mid- and high latitudes where the planetary vorticity is nonnegligible. Climatologically, PV increases with height

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G. Vaughan, J. Methven, D. Anderson, B. Antonescu, L. Baker, T. P. Baker, S. P. Ballard, K. N. Bower, P. R. A. Brown, J. Chagnon, T. W. Choularton, J. Chylik, P. J. Connolly, P. A. Cook, R. J. Cotton, J. Crosier, C. Dearden, J. R. Dorsey, T. H. A. Frame, M. W. Gallagher, M. Goodliff, S. L. Gray, B. J. Harvey, P. Knippertz, H. W. Lean, D. Li, G. Lloyd, O. Martínez–Alvarado, J. Nicol, J. Norris, E. Öström, J. Owen, D. J. Parker, R. S. Plant, I. A. Renfrew, N. M. Roberts, P. Rosenberg, A. C. Rudd, D. M. Schultz, J. P. Taylor, T. Trzeciak, R. Tubbs, A. K. Vance, P. J. van Leeuwen, A. Wellpott, and A. Woolley

The combination of new aircraft measurements and high-resolution modeling reveal finescale wind structure in an intense extratropical windstorm. Extratropical cyclones approaching western Europe along the North Atlantic storm track are a major cause of damaging winds and heavy precipitation. A particular problem in forecasting these cyclones is that the highest-impact weather within them arises from mesoscale structures such as fronts and bands of strong winds. These structures are influenced

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David M. Schultz, Bogdan Antonescu, and Alessandro Chiariello

was discovered during the third author’s master’s thesis research ( Chiariello 2006 ). Therefore, the purpose of this article is to present this cold-type occluded front and to demonstrate its consistency with the static-stability rule. 2. Case study of a cold-type occluded front This case was found from looking at occluded fronts over the North Atlantic Ocean using model output from the European Centre for Medium-Range Weather Forecasts at 0.25° × 0.25° latitude–longitude gridded analyses. At

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Sam Hardy, David M. Schultz, and Geraint Vaughan

sensitivity simulations and introduces the QG omega equation diagnostics. In section 3 , a synoptic overview of the cyclone is presented, including analyses of the cyclone’s intensification ahead of the approaching PV anomaly and the development of the slow-moving frontal rainband over the United Kingdom on 24–25 September. Section 4 then introduces the results from a control simulation of the event using the Advanced Research version of the Weather Research and Forecasting (WRF) Model, which

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C. Dearden, G. Vaughan, T. Tsai, and J.-P. Chen

. 2013a , b ; Sulia et al. 2013 ; Milbrandt and Morrison 2013 ). While Forbes and Clark (2003) explored the diabatic role of ice processes in two rapidly deepening winter cyclones, there is a need to expand the literature on this subject, not just to include a consideration of other winter cases, but also in summer too. Summer cyclones often present more of a challenge to forecasters than winter cyclones; recent examples include the low pressure system from 21 September 2012 spawned by Tropical

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H. F. Dacre, P. A. Clark, O. Martinez-Alvarado, M. A. Stringer, and D. A. Lavers

generates and is enhanced by latent heating associated with cloud processes. Such feedback-driven enhancement is included in our analysis but is not isolated as a separate factor. METHOD. Cyclone tracking and dataset. Following the work of Catto et al. (2010) , we apply an objective cyclone identification and tracking algorithm ( Hodges 1995 ) to fields from the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) for the winter periods (December–February) of

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Oscar Martínez-Alvarado, Laura H. Baker, Suzanne L. Gray, John Methven, and Robert S. Plant

trajectory analysis (see sections 2c and 2d ) requires a large domain to allow long trajectories to be calculated without the majority of them leaving the domain. c. Trajectory analysis Two trajectory models are used in the paper. The first model is the Reading Offline Trajectory Model (ROTRAJ) as developed by Methven (1997) . Its application to aircraft flights is detailed in Methven et al. (2003) . It calculates trajectories using European Centre for Medium-Range Weather Forecasts (ECMWF) analysis

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