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

reference window length of ±20 days ( Fig. 1 ). There is a general tendency for arid climates to perform better for wider windows and for tropical climates to have lowest CRPS for narrower windows, possibly due to effects of seasonal changes such as monsoon onsets. Ultimately, we decided to use the optimal value within the ±5–40-day range for each grid box in order to maximize the skill of EPC over the tropical belt. Fig . 1. CRPS skill of 1-day EPC-based precipitation forecasts using different window

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Joaquim G. Pinto, Florian Pantillon, Patrick Ludwig, Madeleine-Sophie Déroche, Giovanni Leoncini, Christoph C. Raible, Len C. Shaffrey, and David B. Stephenson

characteristics of windstorms in a future climate were the subject of Dominik Büeler’s (ETH Zurich; KIT) presentation. Based on idealized studies, he reported that, while the intensity of moderate cyclones may decrease in a warmer world, an intensification is expected for strong cyclones, which is partly associated with latent heating effects. Such results are of great importance for the insurance industry, as more windstorms have the potential to cause higher losses. PREDICTABILITY AND VARIABILITY FROM

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Joël Arnault, Thomas Rummler, Florian Baur, Sebastian Lerch, Sven Wagner, Benjamin Fersch, Zhenyu Zhang, Noah Kerandi, Christian Keil, and Harald Kunstmann

location of convective cells after a 36-h run. Coupling WRF with the Hydrological Modeling System (HMS; Yu et al. 2006 ), Wagner et al. (2016) investigated groundwater effects on surface and atmospheric variables in a catchment of southeast China for an 8-yr period. Comparing WRF and WRF-HMS precipitation results, basin-averaged differences were minor, although spatial redistribution on the order of ±5% occurred. Rahman et al. (2015) simulated two convective events in western Germany with the

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Stephan Rasp and Sebastian Lerch

might further include seasonal effects. For standard EMOS models, it is possible to account for seasonality by estimating the model based on a centered window around the current day . For the local EMOS model this resulted in negligible improvements only. For postprocessing models with additional predictors seasonal effects can, for example, be included by considering the month of as an input feature. One popular way to combat overfitting in machine learning algorithms is through data

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Roderick van der Linden, Andreas H. Fink, Joaquim G. Pinto, and Tan Phan-Van

mountainous central area north of the study region observed more than 150 mm rain during the first period ( Fig. 4b ). This obvious difference between station measurements and NASA GPM IMERG observations might be related to orographic effects, because these stations are mostly located in windward regions at higher elevations (cf. Fig. 1 ). Another difference is the general dry bias of NASA GPM IMERG ( Fig. 4 ), even though closer scrutiny would need to take the inherent problems of pixel

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Andreas Schlueter, Andreas H. Fink, and Peter Knippertz

. 2009 ). Moisture anomalies starting at low levels, rise to midlevels prior to the convective peak. A stratiform moist outflow is left behind after the passage of the deep convection. In addition to adiabatic heating associated with the vertical circulation, diabatic effects create heating that slows down the wave. The reader is referred to Kiladis et al. (2009) for a more detailed review of the theory, observational evidence, and properties of CCEWs. Two additional disturbances dominate rainfall

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Tobias Selz, Lotte Bierdel, and George C. Craig

-dimensional spectra are then obtained through radial sums of the two-dimensional DCT spectra (see, e.g., Errico 1985 ). In the present study, the spectra are displayed as a function of wavelength , and spectral slopes are obtained with a least squares fit in double-logarithmic space. Horizontal scales smaller than are below the model’s effective resolution and are therefore not considered ( Bierdel et al. 2012 ). The largest spectral mode might be influenced too much by boundary effects and is also discarded

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

. Rainfall over elevated regions is generally higher than in the lowlands. Prominent orographic features include the Guinea Highlands, the Jos Plateau, the Cameroon Line, the Bongo Massif, the Darfur Mountains, and the Ethiopian Highlands as shown in Fig. 1 . It will be demonstrated in section 3e that these orographic features also play a role for how precipitation is modulated by tropical waves. Fig. 3. Mean seasonal precipitation from TRMM observations (1998–2016) during (a) the transition season

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Tobias Selz

here only hold on average over many cases. We used 12 cases distributed over 1 year, which result in 24 midlatitude flow realizations if both hemispheres are considered. However, the intrinsic predictability seems to be flow dependent and varies depending on the metric that has been used up to 10 forecast days, which indicates that there are flow conditions with higher and lower intrinsic predictability. Seasonal and hemispheric subsamples show however only slight differences with higher intrinsic

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

conservation of mass and circulation within a PV isoline on an isentropic surface. The computation of the MLM results in a stronger background flow than the climatological time average and, hence, stronger PV gradients. If one transcends linear theory and accounts for nonlinear effects, the waves do have an impact on the background state. In practice it may, therefore, be an advantage to use a nonstationary background flow, which implicitly accounts for the feedback of the waves on the waveguide. For

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