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Robin J. T. Weber, Alberto Carrassi, and Francisco J. Doblas-Reyes

caused by the displacement of the model state onto the observed values lying outside the model attractor. At the expense of larger initial errors, the objective of AI is to keep the initial state close to the model attractor and reduce the drift. The mean forecast error is less dependent on lead time and, as argued by Magnusson et al. (2013) , the use of standard a posteriori bias correction techniques is more robust. Anomaly initialization can reduce initialization shocks, but is unable to avoid

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Camille Marini, Iuliia Polkova, Armin Köhl, and Detlef Stammer

, which only relay on changes in external forcing ( Smith et al. 2007 ; Pohlmann et al. 2009 ; van Oldenborgh et al. 2012 ; Boer et al. 2013 ). The extra skill in the initialized forecasts is believed to come from climate modes of natural climate variability, which, when properly initialized, carry predictive skill on decadal time scales. Candidate modes of internal climate variability that could contribute to predictive skill encompass Pacific decadal variability and Atlantic multidecadal

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Blake J. Allen, Edward R. Mansell, David C. Dowell, and Wiebke Deierling

filter techniques for radar data assimilation . Mon. Wea. Rev. , 133 , 3081 – 3094 , doi: 10.1175/MWR3021.1 . Chang , D.-E. , J. A. Weinman , C. A. Morales , and W. S. Olson , 2001 : The effect of spaceborne microwave and ground-based continous lightning measurements on forecasts of the 1998 Groundhog Day storm . Mon. Wea. Rev. , 129 , 1809 – 1833 , doi: 10.1175/1520-0493(2001)129<1809:TEOSMA>2.0.CO;2 . Cohen , A. E. , 2008 : Flash rate, electrical, microphysical, and kinematic

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Luke E. Madaus and Gregory J. Hakim

forecasts (e.g., Liu and Xue 2008 ; Sobash and Stensrud 2015 ). To date, this forecast improvement has mostly been attributed to improved representation of broader mesoscale forcings (e.g., fronts or drylines) and reducing near-surface model biases ( Sobash and Stensrud 2015 ). Here we wish to expand these findings by examining the potential for dense surface observations to describe structures on the scale of individual thunderstorms. Surface observations are available at subhourly, kilometer

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Greg Seroka, Travis Miles, Yi Xu, Josh Kohut, Oscar Schofield, and Scott Glenn

unique datasets. Hurricane Irene is an ideal case study because in the days leading up to its landfall in New Jersey (NJ), its intensity was overpredicted by hurricane models (i.e., “guidance”) and in resultant National Hurricane Center (NHC) forecasts ( Avila and Cangialosi 2012 ). The NHC final report on the storm stated that there was a “consistent high bias [in the forecasts] during the U.S. watch–warning period.” NHC attributes one factor in this weakening to an “incomplete eyewall replacement

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Neill E. Bowler, Alberto Arribas, and Kenneth R. Mylne

1. Introduction Ensemble forecasting has its roots in attempts to understand the limits of deterministic prediction of the atmospheric state ( Lewis 2005 ). By running a number of forecasts from a set of initial conditions, which are consistent with our knowledge of the current state of the atmosphere, we hope to gain an insight into the uncertainty in the forecast. Generally, this has been performed by creating a set of perturbations to add to a given best guess (or analysis) of the current

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Ervin Zsoter, Roberto Buizza, and David Richardson

1. Introduction Ensemble prediction systems (EPS) have become one of the key components of the operational forecasting suites at many meteorological institutes. The European Centre for Medium-Range Weather Forecasts (ECMWF; Palmer et al. 1993 ; Molteni et al. 1996 ) and the National Centers for Environmental Prediction [(NCEP), previously the National Meteorological Center (NMC; Tracton and Kalnay 1993 ; Toth and Kalnay 1993 ; Wei et al. 2006 ] implemented the first operational ensemble

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Lee Tryhorn, Amanda Lynch, Rebecca Abramson, and Kevin Parkyn

catastrophes. In contrast, between 1970 and 1990 only three years experienced more than 20 such events. However, this observed increase brings little evidence for a general increase in weather-related mortality, at least until Hurricane Katrina. For example, the total number of flood events in Europe has increased since 1974, while the number of deaths per flood event has decreased, likely due to improved forecasting, improved warning systems, and a greater awareness of risks ( McMichael et al. 2003

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Andreas P. Weigel, Mark A. Liniger, and Christof Appenzeller

1. Introduction Probabilistic forecasts with ensemble prediction systems (EPSs) have found a wide range of applications in weather and climate risk management, and their importance grows continuously. For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) meanwhile operationally applies a 51-member EPS for medium-range weather predictions (e.g., Buizza et al. 2005 ) and a 40-member system for seasonal climate forecasts ( Anderson et al. 2003 ). The rationale behind the

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Michael K. Tippett

1. Introduction The ranked probability score (RPS) is the sum of the squared differences between cumulative forecast probabilities and cumulative observed probabilities, and measures both forecast reliability and resolution ( Murphy 1973 ). The ranked probability skill score (RPSS) compares the RPS of a forecast with some reference forecast such as “climatology” (using past mean climatic values as the forecast), oriented so that RPSS < 0 (RPSS > 0) corresponds to a forecast that is less (more

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