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Volkmar Wirth and Christopher Polster

motivates the present work: we aim to elucidate whether and to what extent the analysis of waveguidability from the zonal-mean flow is adversely affected by the presence of large-amplitude eddies. Following previous authors, we investigate the issue in the framework of the barotropic model. For reference, the model and the related diagnostics are briefly reviewed in section 2 . We then set the stage for the later discussion with the help of a thought experiment in section 3 . In this thought

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

global ensemble systems. Predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF, 2009–17) and the Meteorological Service of Canada (MSC, 2009–16 only due to limited data availability) will be compared, since both performed well in past model intercomparisons over West Africa ( Vogel et al. 2018 ) and Ethiopia by Stellingwerf et al. (2020 ). The analysis will evaluate the whole probability distribution with separate assessments for rainfall occurrence, amount, and extremes. In

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Julian F. Quinting and Christian M. Grams

predictor, we determine the regression coefficient β 1 using 10 times 10-fold cross validation, which provides insights on how the models will generalize to an independent dataset. In this approach, the sets of independent (ERA-Interim based predictors) and dependent observations (WCB inflow, ascent, and outflow regions determined from the trajectory analysis) are divided randomly into 10 folds of approximately equal size. The first fold is used as the validation set, and the model itself is fit on

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Stephan Rasp and Sebastian Lerch

supervised learning task. For the purpose of this study we will consider postprocessing in a narrower distributional regression framework where the aim is to model the conditional distribution of the weather variable of interest given a set of predictors. The two most prominent approaches for probabilistic forecasts, Bayesian model averaging (BMA; Raftery et al. 2005 ) and nonhomogeneous regression, also referred to as ensemble model output statistics (EMOS; Gneiting et al. 2005 ), rely on parametric

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Tobias Selz, Lucas Fischer, and George C. Craig

(right) the nonconvective air mass. In (top), the normalized q υ fields have been divided by 10 to better separate the structure functions of different orders in the plot. The first-order structure function is the topmost dotted line. The red lines show the result of the linear regression analysis in the range between 11 and 100 km according to (4) . The slopes of the lines define the scaling exponents ζ q , which are plotted in (bottom), together with their uncertainty estimation. The red lines

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Thomas Engel, Andreas H. Fink, Peter Knippertz, Gregor Pante, and Jan Bliefernicht

diagnose convective dynamics as well as some statistical tools. The analysis of the atmospheric dynamics of the two selected cases will be presented in section 4 , while section 5 will provide the results of the statistical evaluation of the long-term context. Section 6 summarizes our findings with concluding remarks and an outlook on future investigations. 2. Data a. Gauge data Rain gauges are the most reliable method of measuring the precipitation amount and offer a high temporal resolution. In

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

-line occurrence in the western Sahel ( Fink and Reiner 2003 ) and lead to enhanced skill in cloudiness forecasts over West Africa ( Söhne et al. 2008 ). However, numerical weather prediction (NWP) models are known to have an overall poor ability to predict rainfall systems over northern Africa. For example, the gain in skill by improved initial conditions due to an enhanced upper-air observational network during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign ( Parker et al. 2008 ) was

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

J. Methven , 2013 : Diabatic processes modifying potential vorticity in a North Atlantic cyclone . Quart. J. Roy. Meteor. Soc. , 139 , 1270 – 1282 , . 10.1002/qj.2037 Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis . J. Atmos. Sci. , 50 , 2038 – 2053 ,<2038:DDOBWA>2.0.CO;2 . 10.1175/1520-0469(1993)050<2038:DDOBWA>2.0.CO;2 Chang , E. K. M. , 1999

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Georgios Fragkoulidis and Volkmar Wirth

, 1893 – 1910 ,<1893:CTTJSW>2.0.CO;2 . 10.1175/1520-0442(2002)015<1893:CTTJSW>2.0.CO;2 Cai , M. , and B. Huang , 2013 : A dissection of energetics of the geostrophic flow: Reconciliation of Rossby wave energy flux and group velocity . J. Atmos. Sci. , 70 , 2179 – 2196 , . 10.1175/JAS-D-12-0249.1 Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis . J

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Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau Jr., Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, and Fuqing Zhang

-0272.1 Kowaleski , A. M. , and J. L. Evans , 2016 : Regression mixture model clustering of multimodel ensemble forecasts of Hurricane Sandy: Partition characteristics . Mon. Wea. Rev. , 144 , 3825 – 3846 , . 10.1175/MWR-D-16-0099.1 Kumpf , A. , M. Rautenhaus , M. Riemer , and R. Westermann , 2019 : Visual analysis of the temporal evolution of ensemble forecast sensitivities . IEEE Trans. Visualization Comput. Graphics , 25 , 98 – 108 , https

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