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Robert Redl, Andreas H. Fink, and Peter Knippertz

Abstract

Convective cold pool events in (semi) arid areas have significant impacts on their environment. They reach horizontal extents of up to several hundred kilometers and the associated turbulence and shear can cause dust emissions and threaten aviation safety. Furthermore, cold pools play a major role in the organization of deep convection and in horizontal moisture transport. They have even been proposed to have impacts on larger-scale monsoon dynamics. Cold pools are not well represented in models using a convective parameterization. To test and improve these models, it is necessary to reliably detect cold pool occurrence from standard observational data. Former studies, however, focused on single cases or short time periods.

Here, an objective and automated method for the generation of multiyear climatologies of cold-pool events is presented. The algorithm combines standard surface observations with satellite microwave data. Representativeness of stations and influence of their spatial density are addressed by comparison to a satellite-only climatology. Applying this algorithm to data from automatic weather stations and manned synoptic stations in and south of the Atlas Mountains in Morocco and Algeria reveals the frequent occurrence of cold pool events in this region. On the order of six cold-pool events per month are detected from May to September when the Saharan heat low is in its northernmost position. The events tend to cluster into several-days-long convectively active periods, often with strong events on consecutive days. The algorithm is flexible enough to be applied in comparable regions around the world.

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George C. Craig, Andreas H. Fink, Corinna Hoose, Tijana Janjić, Peter Knippertz, Audine Laurian, Sebastian Lerch, Bernhard Mayer, Annette Miltenberger, Robert Redl, Michael Riemer, Kirsten I. Tempest, and Volkmar Wirth

Abstract

Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-year phase of our planned duration of 12 years and employ 36 doctoral and post-doctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely upscale error growth, errors associated with cloud processes, and probabilistic prediction of high impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early career scientists, and to support education, equal opportunities, and outreach, which are also described here.

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