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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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Stephen B. Cocks and William M. Gray

to compare this technique to techniques currently in use in the western Pacific. Presently, the JTWC uses a combination of climatological parameters to estimate a radius of 17.5 m s −1 winds (where R17 is a persistent artifact of 35-kt wind forecast technology) in lieu of in situ data such as ship and island reports or scatterometer measurements. One such method includes using infrared satellite imagery to infer the area of deep convection from cold (−65°C) cloud tops extending from the center

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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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Joseph Bellier, Isabella Zin, and Guillaume Bontron

definition encompasses forecasts issued by meteorological ensemble forecasting techniques ( Buizza et al. 1999 ) but also probabilistic forecasts issued by other forecasting techniques such as statistical adaptations, like the analog method ( Obled et al. 2002 ; Hamill and Whitaker 2006 ), or single-value forecast dressings ( Schaake et al. 2007 ). Two widely used verification measures for ensemble forecasts are the continuous ranked probability score (CRPS) ( Matheson and Winkler 1976 ; Hersbach 2000

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Rene Orth, Emanuel Dutra, and Florian Pappenberger

and other challenges such as increased uncertainty in the model structure. Furthermore, it is challenging to apply model parameterizations at the required spatial scales ( Kauffeldt et al. 2013 , 2015 ). The calibration of model parameters is moreover complicated by the scarcity (and uncertainty) of observations of land surface hydrology and of basic characteristics of the soils and the vegetation. In this study we focus on the European Centre for Medium-Range Weather Forecasts (ECMWF) land

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