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Andrei Bourchtein

includes also the reference to BDM balance equations. One of the problems of NMI method, discovered by Daley (1978) , is divergence of iterative algorithm for solution of balance equations in the case when the geopotential field is held unchanged (the so-called geopotential constrained initialization). The discussions about the reasons behind this problem were centered about two possibilities: convergence properties of the applied iterative algorithm and mathematical nature of the balance

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Srdjan Dobricic

removed in the “weak-constraint” 4DVAR algorithm. If the state vector is defined on the model space, the size of the parameter space with the imperfect model may increase significantly (e.g., Bennett 1992 ; Courtier 1997 ), although the computational effort can be made equal to that of the 4DVAR with the perfect model assumption ( Tremolet 2006 ). The problem of the size of the parameter space may be a minor problem in operational meteorology and oceanography, because in that case the assimilation

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Yulong Bai and Xin Li

techniques have to deal with this same problem. Therefore, to improve the error parameterization efficiency and the output accuracy of DA systems, the motivations of this paper are trying to unify the common error parameterization methods from the methodological point of view and to choose the error factors more reasonably. Inspired by modern biological evolution, an evolutionary algorithm is an evolutionary computation technique for searching and optimization; it can simulate natural evolutionary

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Mikhail Ovtchinnikov and Richard C. Easter

spatial distribution of advected variables (e.g., Chlond 1994 ; Walcek 2000 , hereinafter W00 ). Although these modifications provide the algorithms with desired properties such as monotonicity and improved gradient preservation, they also make these algorithms nonlinear. Consequently, any relations among interrelated tracers advected separately are not necessarily preserved. This presents a serious problem for models in which variables derived from several tracers represent important properties

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Yulong Xing, Andrew J. Majda, and Wojciech W. Grabowski

this paper is to present new efficient sparse space–time algorithms for superparameterization (SSTSP), which require less computational cost and yet provide statistically accurate large-scale features. We note that the small-scale models are actually solved over the whole time in the original superparameterization, which occupies the most computational time. In the new efficient algorithm, we can solve the small-scale models for a reduced partial time and over a reduced periodic domain. The

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R. G. Hanea, G. J. M. Velders, A. J. Segers, M. Verlaan, and A. W. Heemink

eigenvectors. Because of the reduction, the covariance matrix is always underestimated; although this bias reduces with the number of modes. As a result, the algorithm is sensitive to filter-divergence problems. The RRSQRT approach can be viewed as an EnKF for which the modes are not chosen randomly, but in the direction of the largest eigenvectors. In both cases the number of modes represents a measure of the storage and computation time required by the filter, and should be as low as possible, while

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Tijana Janjić, Lars Nerger, Alberta Albertella, Jens Schröter, and Sergey Skachko

1. Introduction The ensemble-based Kalman filter approach has been widely used for data assimilation in both meteorology and oceanography (see, e.g., Houtekamer and Mitchell 1998 , 2001 ; Brankart et al. 2003 ). In the ensemble Kalman filter algorithms, the forecast error covariance matrix is approximated by a covariance matrix whose rank is 1 less than the number of ensemble members. For computational tractability, the number of ensemble members, and therefore the rank of the covariance

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Ali R. Mohebalhojeh and David G. Dritschel

1. Introduction The simultaneous use of “grid” and “contour” representations for the materially conserved field of potential vorticity (PV), or some approximation to PV-like quasigeostrophic PV, is the main novel feature in the contour advective semi-Lagrangian (CASL) algorithms developed since the original work by Dritschel and Ambaum (1997) . The PV field is assumed to have a discrete distribution (i.e., a number of level sets divided by contours or PV jumps). Mathematically, the discrete

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Hristo G. Chipilski, Xuguang Wang, and David B. Parsons

et al. 2017 ). The dynamical significance of convective outflow boundaries has prompted the scientific community to create automated algorithms for identifying and tracking these features. The earliest algorithm developed for this purpose was entirely based on observational data and closely connected to the procurement plans for the Next Generation Weather Radar (NEXRAD) system (e.g., Crum and Alberty 1993 ). In particular, Uyeda and Zrnić (1986) as well as Smith et al. (1989) were the first

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Mariusz Starzec, Cameron R. Homeyer, and Gretchen L. Mullendore

-altitude levels from three-dimensional composites of multiple ground-based radars to distinguish between convective, stratiform, and anvil clouds. While the aforementioned studies incorporated vertical storm information in the SHY procedure, the primary classification between convective and stratiform precipitation in SHY-based algorithms and similar approaches is completed using a single low-altitude map of . For research purposes such as quantitative precipitation estimation, SHY-based methods applied to

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