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

we shall give a brief outline of the tracking method used. See Hewson and Titley (2010) for a full description of the objective feature identification and tracking methodology. The tracking algorithm aims to identify and track developing cyclonic storms through their entire life cycle from small kinks in fronts (labeled diminutive frontal waves) through developed frontal waves to closed low pressure centers (labeled barotropic lows). The algorithm uses a hybrid of objective fronts, pressure

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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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Jesse Norris, Geraint Vaughan, and David M. Schultz

indicated in this figure (i.e., the same part of the cold front). Also shown is the location of the front, identified from the algorithm detailed in section 4 (rearmost bold line) and the line obtained by moving 6 grid cells (24 km) forward for each y coordinate. Various diagnostics are calculated between these two lines throughout the paper. Fig . 4. Evolution of surface precipitation rate (colors, mm h −1 ) and 10-m relative vorticity (contours at 5, 10, 20, 50, and 100 × 10 −5 s −1 ) in the

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Oscar Martínez-Alvarado, Suzanne L. Gray, and John Methven

. Quart. J. Roy. Meteor. Soc. , 125 , 1607 – 1636 , doi: 10.1002/qj.49712555707 . Zhu , Y. , and R. E. Newell , 1998 : A proposed algorithm for moisture fluxes from atmospheric rivers . Mon. Wea. Rev. , 126 , 725 – 735 , doi: 10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2 .

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Ross N. Bannister

, 2007 : Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter . Physica D , 230 , 112 – 126 , doi: 10.1016/j.physd.2006.11.008 . Janjić , T. , L. Nerger , A. Albertella , J. Schröter , and S. Skachko , 2011 : On domain localization in ensemble-based Kalman filter algorithms . Mon. Wea. Rev. , 139 , 2046 – 2060 , doi: 10.1175/2011MWR3552.1 . Kepert , J. , 2009 : Covariance localization and balance in an ensemble Kalman filter . Quart

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