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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

current atmospheric state (e.g., tropical waves or influences from the extratropics) into meaningful impacts regarding the occurrence or amount of precipitation. This is robust for verification against station as well as satellite observations and cannot, therefore, be explained by propagation errors. For longer accumulation times and larger spatial aggregations, the large-scale circulation has a much stronger impact on convective activity, which should weaken the limitations through convective

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Stephan Rasp, Tobias Selz, and George C. Craig

1. Introduction Diabatic processes in the atmosphere, especially the release of latent heat through condensation and freezing, have been shown to have a large impact on atmospheric dynamics by modifying the upper-tropospheric potential vorticity (PV) distribution. Warm conveyor belts (WCB) are the predominant diabatically influenced phenomena in the midlatitudes. They are defined as broad airstreams that originate from the boundary layer of the cyclone’s warm sector and subsequently rise along

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Michael Maier-Gerber, Michael Riemer, Andreas H. Fink, Peter Knippertz, Enrico Di Muzio, and Ron McTaggart-Cowan

1. Introduction Tropical transition (TT) describes the phenomenon when a tropical cyclone (TC) emerges from an extratropical cyclone ( Davis and Bosart 2003 , 2004 ). During TT, the extratropical cyclone transforms from a cold- to a warm-core system. A cascade of events commonly precedes the TT: anticyclonic wave breaking (e.g., Thorncroft et al. 1993 ; Postel and Hitchman 1999 ) causes an upper-level precursor potential vorticity (PV) trough to penetrate into the (sub)tropics ( Galarneau et

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Tobias Selz

background spectrum and thus likely involve a climatological component. For example, orography and the land–sea distribution may hinder the largest planetary waves from freely evolving. In addition the ICON simulations have fixed sea surface temperatures. d. Comparison to simulations with a deterministic convection scheme A second set of simulations has been performed using the ICON model but this time in its standard setup with the deterministic TB convection scheme ( Bechtold et al. 2001 ). With this

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Kevin Bachmann, Christian Keil, George C. Craig, Martin Weissmann, and Christian A. Welzbacher

based on the procedures currently available” ( Lorenz 1969 ; Melhauser and Zhang 2012 ). Similar to Surcel et al. (2015) , we further distinguish the predictability of the model state, measured by ensemble dispersion, and the model predictability of the atmospheric state, incorporating a comparison to observations. The latter therefore includes model and observing system errors and represents a classic practical predictability estimate, while the predictability of the model state describes how

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Kirstin Kober and George C. Craig

1. Introduction Forecasting convective initiation more than a few hours in advance is an ongoing challenge in atmospheric research. The exact timing and location will probably not be forecast by numerical weather prediction (NWP) models in the near future, but forecasts of the probability of precipitation can show useful skill. Probabilistic forecasts aim to represent uncertainty that results from several sources of varying importance. The intrinsic uncertainty of a chaotic system like the

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Hilke S. Lentink, Christian M. Grams, Michael Riemer, and Sarah C. Jones

Center “Waves to Weather” (TRR 165), project A4: “Evolution and predictability of storm structure during extratropical transition of tropical cyclones.” Observational data were obtained in the framework of T-PARC. We thank the international consortium that supported the T-PARC field campaign and acknowledge the involvement of the U.S. National Science Foundation (NSF)–sponsored National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) for data management and quality control

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Christian Barthlott and Corinna Hoose

atmospheric dynamics and thermodynamics ( Fan et al. 2016 ). In general, it is assumed that additional aerosols acting as cloud condensation nuclei (CCN) result in more numerous and smaller cloud droplets. The increased reflectance of these brighter clouds is known as the albedo effect or the Twomey effect ( Twomey 1977 ). The reduced droplet size and the narrower droplet spectrum suppress the onset of precipitation in warm clouds as result of the less efficient collision–coalescence process. This results

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Christian Euler, Michael Riemer, Tobias Kremer, and Elmar Schömer

Small-Scale Modeling (COSMO; Steppeler et al. 2003 ) model, version 5.04. COSMO is a nonhydrostatic limited-area atmospheric prediction model. The model is based on nonhydrostatic hydro-thermodynamical equations for fully compressible flow and it has been designed for both operational numerical weather prediction and scientific applications. Convection-permitting simulations with the COSMO model have been successfully used before to investigate structure changes of a TC during ET ( Lentink et al

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Stephan Rasp, Tobias Selz, and George C. Craig

1. Introduction Stochastic parameterizations have the potential to increase forecast skill and decrease model biases by capturing the inherently turbulent nature of many subgrid processes [for a comprehensive overview, see Berner et al. (2016) ]. In the case of atmospheric deep convection, the fluctuations around the mean state within a grid box become significant for model grid spacing less than 100 km ( Jones and Randall 2011 ). This subgrid noise can feed back onto the resolved scales

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