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Franziska Teubler and Michael Riemer

from a preexisting upper-level EKE center, usually identified as a jet streak in the vicinity of a trough, downstream development is described by the downstream energy dispersion due to ageostrophic geopotential fluxes. Subsequent to such initial growth, the new EKE center downstream is further amplified by baroclinic energy conversion. Then, downstream radiation of energy initiates the decay of the new EKE center. The cycle may then repeat itself in the region farther downstream. In addition

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Volkmar Wirth, Michael Riemer, Edmund K. M. Chang, and Olivia Martius

conservation of total energy following the 3D flow); is the velocity vector, ω is the pressure vertical velocity, and α is the specific volume. In (3) , the first two terms on the right-hand side represent baroclinic and barotropic conversion, respectively. The third term on the right-hand side also represents an energy transfer between the mean flow and the eddies, but averages out to zero in the time mean. Following Orlanski and Sheldon (1993) , the energy flux can be written as follows: where

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Kirstin Kober, Annette M. Foerster, and George C. Craig

formulation of a parameterization based on theory ( Plant and Craig 2008 , hereafter PC08 ). The application of stochastic parameterizations has shown improved skill ( Lin and Neelin 2003 ) and increased spread in the ensemble forecasts ( Buizza et al. 1999 ). The stochastic convection parameterization by PC08 is based on the Craig and Cohen (2006) theory and high-resolution simulations of radiative convective equilibrium. The scheme was successfully tested in single-column mode ( PC08 ) and in an

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Andreas Schäfler, Andreas Dörnbrack, Christoph Kiemle, Stephan Rahm, and Martin Wirth

2008 ; Miglietta and Rotunno 2009 ), or low-level moisture supply ( Boutle et al. 2010 ; Keil et al. 2008 ), and their physical representation in NWP models has emerged to play a crucial role for QPF. In particular, the supply of low-level moisture by latent heat fluxes or through advective transport is crucial for the evolution of midlatitude weather systems. As pointed out by Boutle et al. (2010) , large-scale moisture advection is the process that maintains the structure of the boundary layer

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Tobias Selz and George C. Craig

activity is mainly driven by large-scale forced ascent or strong local surface fluxes; Done et al. 2006) . Although the growth rate of small-scale errors is much higher than for synoptic-scale errors, they are also smaller in energy and saturate much faster than the slower-growing errors on large scales. Further upscale growth is then much slower and the impact of the small-scale errors is reduced. On the other hand, the initial state uncertainty is much smaller at the synoptic scale, which might

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Marlene Baumgart, Michael Riemer, Volkmar Wirth, Franziska Teubler, and Simon T. K. Lang

amplification. The according tendency equation is given by To provide a succinct, quantitative view on the error growth, we consider the error evolution integrated over a (potentially) time-dependent area A : where v S describes the motion of the integration area. As in Boer (1984) , the first term on the right-hand side of Eq. (5) can be interpreted as a (nonlinear) production term. Since the second term in Eq. (4) merely redistributes existing errors, it can be evaluated in terms of an error flux

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