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Tobias Selz, Lotte Bierdel, and George C. Craig

) for making COSMO-DE analyses and ERA5 data available. The authors are grateful to three anonymous reviewers for their helpful comments. REFERENCES Achatz , U. , B. Ribstein , F. Senf , and R. Klein , 2017 : The interaction between synoptic-scale balanced flow and a finite-amplitude mesoscale wave field throughout all atmospheric layers: Weak and moderately strong stratification . Quart. J. Roy. Meteor. Soc. , 143 , 342 – 361 , https://doi.org/10.1002/qj.2926 . 10.1002/qj.2926

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Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model

Julian F. Quinting and Christian M. Grams

.: Comput. Stat. , 2 , 370 – 374 , https://doi.org/10.1002/wics.84 . 10.1002/wics.84 Altenhoff , A. M. , O. Martius , M. Croci-Maspoli , C. Schwierz , and H. C. Davies , 2008 : Linkage of atmospheric blocks and synoptic-scale Rossby waves: A climatological analysis . Tellus , 60A , 1053 – 1063 , https://doi.org/10.1111/j.1600-0870.2008.00354.x . 10.1111/j.1600-0870.2008.00354.x Baumgart , M. , M. Riemer , V. Wirth , F. Teubler , and S. T. K. Lang , 2018 : Potential

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Tobias Selz, Lucas Fischer, and George C. Craig

1. Introduction The complex interaction of water vapor with atmospheric motion and mixing processes over a wide range of spatial scales, together with various sinks and sources, leads to a very heterogeneous humidity distribution in the troposphere. A better understanding of the spatial variability of water vapor in the free troposphere is essential for the representation of clouds, including fractional cloud cover, in numerical weather prediction models (NWP) and global circulation models (GCM

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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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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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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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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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