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

horizon to few days at scales of 100 km ( Judt 2020 ). Third, small-scale physical processes such as cloud microphysics and radiation can relatively easily affect scales large enough to be of interest to predictions through their effects on the vertical profiles of latent (and radiative) heating and thus divergent wind. For example, convective invigoration by increased cloud condensation nuclei ( Rosenfeld et al. 2008 ) and larger or longer-lived anvils ( Fan et al. 2013 ) affect convective

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Tobias Selz, Lucas Fischer, and George C. Craig

) (e.g., Stevens and Bony 2013a ). Cloud properties are strongly dependent on the humidity distribution of the ambient air, which in nature varies significantly over distances corresponding to the size of a model grid box ( Cusack et al. 1999 ; Tompkins 2002 ; Wang et al. 2010 ). Due to the fact that clouds reflect solar radiation back to space and trap infrared radiation emitted by the surface, even small differences in modeling cloud feedbacks can have a strong influence on climate simulations

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Andreas Schäfler, George Craig, Heini Wernli, Philippe Arbogast, James D. Doyle, Ron McTaggart-Cowan, John Methven, Gwendal Rivière, Felix Ament, Maxi Boettcher, Martina Bramberger, Quitterie Cazenave, Richard Cotton, Susanne Crewell, Julien Delanoë, Andreas Dörnbrack, André Ehrlich, Florian Ewald, Andreas Fix, Christian M. Grams, Suzanne L. Gray, Hans Grob, Silke Groß, Martin Hagen, Ben Harvey, Lutz Hirsch, Marek Jacob, Tobias Kölling, Heike Konow, Christian Lemmerz, Oliver Lux, Linus Magnusson, Bernhard Mayer, Mario Mech, Richard Moore, Jacques Pelon, Julian Quinting, Stephan Rahm, Markus Rapp, Marc Rautenhaus, Oliver Reitebuch, Carolyn A. Reynolds, Harald Sodemann, Thomas Spengler, Geraint Vaughan, Manfred Wendisch, Martin Wirth, Benjamin Witschas, Kevin Wolf, and Tobias Zinner

on quantifying model errors and predictability, and in particular on investigating the role of diabatic processes such as those related to clouds and radiation, whose interaction with the dynamics of the flow must be understood and represented more accurately in models in order to further improve forecast quality. Detailed observations are needed to characterize the weather systems and embedded physical processes across a range of spatial and temporal scales that encompass cloud microphysical

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Andreas Schlueter, Andreas H. Fink, and Peter Knippertz

detail in Part I . As in Part I , the Climate Hazards Group Infrared Precipitation with Station data, version 2 (CHIRPS), dataset was used to visualize the associated rainfall anomaly patterns. CHIRPS estimates daily precipitation rates over landmasses only by gauge calibration of infrared measurements and has a spatial resolution of 0.25° × 0.25° ( Funk et al. 2015 ). Reanalyses have large biases in humidity fields but also in wind and temperature over the measurement-sparse African continent

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

.25° × 0.25°. The analyzed time period ranges from 1998 to 2016. With full spatial coverage in the tropics and a high temporal and spatial resolution, the TRMM dataset is a preferable dataset for the study of tropical waves in a rain gauge sparse environment such as tropical Africa. For a 33-yr period from 1981 to 2013, the Climate Hazards Group Infrared Precipitation with Station Data V.2 (CHIRPS) provides daily, gauge-calibrated, infrared-based precipitation estimates ( Funk et al. 2015 ). The

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