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Nusret Balci, Anna L. Mazzucato, Juan M. Restrepo, and George R. Sell

multiplicative ergodic theorem (MET) and the finite-time singular vectors to approximate the Lyapunov vectors. The BV algorithm is a finite-time, forward sensitivity methodology that, in addition to being useful in characterizing model sensitivity to initial conditions, has been proposed as a means to produce a reduced-rank representation of the background error in data assimilation and forecast error-covariance approximations (see, e.g., Corazza et al. (2003) . Several articles in the literature have

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Ruiqiang Ding, Jianping Li, and Kyong-Hwan Seo

tropical and extratropical circulation anomalies, it is of great interest to investigate its upper limit of predictability. For example, information on the MJO predictability limit could be used as a basic guideline for the future improvement of forecast models. However, the limit of the predictability of the MJO phenomenon remains an open issue. A number of recent studies have demonstrated that the useful skill of the MJO in existing numerical and statistical models can reach about 15–20 days (e

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Thorsten Simon, Peter Fabsic, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis

technique can be found in Mayr et al. (2012) . However, selecting the right-sized subset of covariates ( Meinshausen and Bühlmann 2010 )—that is, avoiding selecting some noise variables ( Hofner et al. 2015 )—remains challenging. A solution to this issue is combining gradient boosting as a method of regularization with stability selection ( Hofner et al. 2015 ). The aim of this study is to develop a probabilistic forecasting method for the occurrence of thunderstorms in the eastern Alps and their

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Chiara Piccolo and Mike Cullen

) or seeking a stochastic formulation of the parameterization schemes (e.g., Palmer 2001 ). The limitation of this approach is that it is usually empirical and, ideally, should be calibrated using data assimilation techniques such as those we describe. However, this calibration can only be performed in a climatological sense for the same reasons as above. Stochastic physics approaches are normally used in ensemble forecasts rather than ensemble data assimilation algorithms. In the latter case, it

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

1. Introduction The ensemble Kalman filter (EnKF) is a popular method for doing data assimilation (DA) in the geosciences. This study is concerned with the treatment of model noise in the EnKF forecast step. a. Relevance and scope While uncertainty quantification is an important end product of any estimation procedure, it is paramount in DA because of the sequentiality and the need to correctly weight the observations at the next time step. The two main sources of uncertainty in a forecast are

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Gregor Robinson, Ian Grooms, and William Kleiber

show that the particle filter’s uncertainty quantification is improved by the GRF likelihood: a 25% decrease (improvement) in CRPS is comparable to the improvement achieved by various statistical postprocessing techniques for ensemble forecasts ( Kleiber et al. 2011a , b ; Scheuerer and Büermann 2014 ; Feldmann et al. 2015 ). Somewhat surprisingly, the CRPS significantly improves moving from to despite the fact that the ESS remains quite small. Overall, these CRPS results suggest that even

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Lindley Graham, Troy Butler, Scott Walsh, Clint Dawson, and Joannes J. Westerink

communities, data assimilation techniques based upon the ensemble Kalman filter are perhaps the most commonly used methods for estimating both parameters and state variables of models ( Mayo et al. 2014 ; Aksoy 2015 ; Ruiz et al. 2013 ). The attractiveness of these type of data assimilation methods are both their ease of implementation and ability to provide so-called real-time updates to parameter estimates and state variable forecasts as new data become available. In Ruiz et al. (2013) , it was shown

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Ben Jolly, Adrian J. McDonald, Jack H. J. Coggins, Peyman Zawar-Reza, John Cassano, Matthew Lazzara, Geoffery Graham, Graeme Plank, Orlon Petterson, and Ethan Dale

Mountains presents a significant barrier, rising to 2000 m above sea level, and the boundary layer in this area is usually stably stratified, therefore, barrier winds are extremely common ( Parish et al. 2006 ; Seefeldt and Cassano 2012 ). Recent work by Nigro and Cassano (2014a) using output from the polar-modified Weather Research and Forecasting (WRF) Model in the Antarctic Mesoscale Prediction System (AMPS) showed that a PGF conducive to barrier-parallel flow is sometimes produced by the

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Roman Schefzik

given by b. Reference methods Now we review ECC ( Schefzik et al. 2013 ) and the Schaake shuffle ( Clark et al. 2004 ) as empirical copula-based postprocessing techniques within the scheme described above. 1) Ensemble copula coupling In the ECC approach ( Schefzik et al. 2013 ), the dataset specifying the dependence structure is given by the raw ensemble forecast; that is, we have in the scheme from section 2a . Depending on the chosen sampling procedure in step 3, one can distinguish between the

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Bert Van Schaeybroeck and Stéphane Vannitsem

1. Introduction The use of ensemble predictions allows for the estimation of possible outcomes of specific weather events. From the ensemble, a “best” deterministic forecast can be extracted as the ensemble mean ( Ehrendorfer 1997 ), while the forecast uncertainty is often reduced to one measure, called ensemble spread , summarizing the information on the uncertainties associated with the impact of the presence of the initial conditions and model errors ( Nicolis et al. 2009 ). Although much

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