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Chengsi Liu and Ming Xue

implicitly evolves the BEC within the DA window so that it can be flow dependent, but the BEC itself defined at the beginning of the DA window is usually static and is not propagated from one DA window to another ( Lorenc 2003 ). EnKF estimates flow-dependent BEC from a set of ensemble forecasts and updates the ensemble states based on an optimal linear estimation algorithm ( Evensen 1994 ). Because EnKF estimates and evolves flow-dependent BEC within and through the data assimilation cycles, and does

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Daryl T. Kleist and Kayo Ide

–variational (EnVar)-based algorithm; Lorenc 2013 ] 1 although it is possible that one could utilize an alternate framework [e.g., an ensemble Kalman filter (EnKF)]. Many of these hybrid methods with technically different algorithms have been shown to be theoretically equivalent, whether using a combined covariance through brute force or through a variational-based control variable method ( Wang et al. 2007a ). Various studies have demonstrated that the hybrid algorithm can in fact improve upon stand

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Daryl T. Kleist and Kayo Ide

ensemble covariances in a variational-based hybrid algorithm yielded improvements in the quality of analyses and subsequent forecasts for the National Centers for Environment Prediction (NCEP) Global Forecast System (GFS) model in the context of an observing system simulation experiment (OSSE). The experiments were performed using 3DVar and hybrid 3DEnVar, leaving significant room for improvement. Without access to the TL and AD models, a natural extension of the hybrid 3DEnVar to include 4D ensemble

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Patrick Nima Raanes, Alberto Carrassi, and Laurent Bertino

-Gaussian case, these characterize and , and are given, recursively in time for sequentially increasing indices, t , by the Kalman filter equations. The EnKF is an algorithm to approximately sample ensembles, , from these distributions. Note that the positive integer N is used to denote ensemble size, while m and p have been used to denote state and observation vector lengths. For convenience, all of the state realizations are assembled into the “ensemble matrix”: A related matrix is that of the

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Jean-François Caron, Thomas Milewski, Mark Buehner, Luc Fillion, Mateusz Reszka, Stephen Macpherson, and Judy St-James

2014, we described the modifications to the Regional Deterministic Prediction System (RDPS), a limited-area system providing NWP guidance up to day 2 using an LAM covering North America with an approximate 10-km horizontal grid spacing. The main modification is the replacement of the limited-area 4DVar algorithm for the LAM analysis and the associated 3DVar scheme for the synchronous global driver analysis by the same 4D ensemble–variational (4DEnVar) algorithm implemented in the GDPS ( Part I

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Yicun Zhen and Fuqing Zhang

correlation together with a nonadaptive empirical localization function. The method proposed in the current study is based on a sequential ensemble square root filter. For covariance inflation we utilize the relaxation method from Zhang et al. (2004) . To clarify, we present the algorithm in detail in Table 1 . Moreover we would like to mention that the method under discussion in this manuscript only applies to the case when observations are uncorrelated. Further generalizations of this approach to

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Lars Nerger

assimilate all observations synchronously. However, the EAKF and the EnSRF are typically described to assimilate single observations serially, which increases the efficiency of these filter formulations. Further, both algorithms are algorithmically identical in case of serial observation processing. For example, the DART assimilation system ( Anderson et al. 2009 ) provides an EAKF with serial observation processing. Localization of covariance matrices in ensemble-based Kalman filters is required for

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Stefano Migliorini

clouds and no need for cloud detection (see, e.g., Bauer et al. 2010 ). The paper is structured as follows. Section 2 provides a detailed description of the channel selection methodology and a step-by-step algorithm. Also in this section, the standard information-based figure of merit used for selection is extended to allow a selection that is optimal for estimation over a subspace of the state space (e.g., over a given height range or a given parameter). In section 3 a description of the case

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Daisuke Hotta, Tse-Chun Chen, Eugenia Kalnay, Yoichiro Ota, and Takemasa Miyoshi

EFSO’s capacity to identify detrimental innovations that actually degrade the forecast skill. We first perform DA using all the available observations that passed the standard QC and 6 h later we compute regional 6-h forecast errors (with respect to the analysis) and apply an algorithm to detect regional skill dropouts. We next conduct EFSO diagnostics on the detected regions to identify potential detrimental innovations that are likely responsible for the regional dropouts. Finally, we repeat

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Shigenori Otsuka and Takemasa Miyoshi

), denotes the i th nonlinear model operator from time to t , and denotes the full multimodel ensemble mean. Hereafter, t is mostly omitted for convenience. Next, the forecast error covariance is computed as follows: where denotes the ensemble perturbation matrix. The SEnSRF algorithm determines the updated analysis state and covariance as follows: where denotes the observations, is the Kalman gain, is the gain used to update , is the linear observation operator, and is the

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