Since 2015 DFG funds the Collaborative Research Center 165 “Waves to Weather” (W2W). Lead institution is the Ludwig-Maximilians University (LMU) in Munich. KIT and the Johannes Gutenberg University (JGU) in Mainz are the two most important partners. Other participants are the Technical University Munich (TUM), the German Aerospace Center (DLR) and the University of Heidelberg.
The consortium addresses the great challenge of identifying the limits of predictability and of producing the best forecasts that are physically possible. In particular, W2W investigates some of the most important causes of uncertainties in weather prediction:
- the quick upscale growth of forecast errors from insufficiently resolved or represented processes like convection or boundary layer mixing, which modify synoptic-scale waves
- our limited understanding of processes in clouds, and
- the influence of local factors on weather that influence the predictability associated with larger-scale wave disturbances.
W2W addresses these three areas in a concerted effort involving contributions from the disciplines of atmospheric dynamics, cloud physics, statistics, inverse methods and visualization. W2W will use, and further develop a broad range of tools, including numerical models with detailed treatment of cloud processes and aerosols, ensemble forecasts with sophisticated statistical postprocessing and new interactive visualization methods to describe and identify uncertainty.
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The WG “Atmospheric Dynamics” makes the largest contribution from KIT to “Waves to Weather”. Peter Knippertz is coordinator for the location Karlsruhe, Andreas Fink is coordinator of Reseach Area C – “Predictability of local weather”.
- C2 - Prediction of wet and dry periods of the West African monsoon
- C3 - Multi-scale dynamics and predictability of Atlantic Subtropical Cyclones and Medicanes
- C4 - Coupling of planetary-scale Rossby wave trains to local extremes in heat waves over Europe
- C5 - Forecast uncertainty for peak surface gusts associated with European cold-season cyclones
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The WG “Cloud Physics“ leads two sub-projects of “Waves to Weather“.
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B1 - Microphysical uncertainties in deep convective clouds and their implications for data assimilation (PIs: Prof. Dr. Corinna Hoose, PD. Dr. Michael Kunz, Dr. Bernhard Vogel; Post-doc: Dr. Andrew Barrett; PhD student: Constanze Fischerkeller)
The liquid/ice phase partitioning in convective clouds and the generation of precipitation via the ice phase are currently poorly predicted in operational weather forecast models. Especially, supercooled cloud droplets and frozen hydrometeors are poorly represented although being important for the prediction of severe hailstorms with a high damage potential and for aviation forecasts. In addition, the realistic representation of clouds and hydrometeors is essential for the assimilation of remote-sensing observations related to these quantities. Within this project, we will identify which microphysical processes have to be included or refined in numerical weather prediction (NWP) models to improve our ability to predict the convective cloud phase and resulting precipitation with a special focus on hail. Besides the direct forecast impact, this shall advance our capability to use the information provided by potential high-impact observations such as radar reflectivity and cloudy satellite radiances and reflectances in the visible and infrared spectrum in convective-scale data assimilation.
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B3 - Relative impact of surface and aerosol heterogeneities on the initiation of deep convection (PIs: Dr. Christian Barthlott, Prof. Dr. Corinna Hoose; PhD student: Linda Schneider)
This project investigates the relative contribution of orographic features, land surface heterogeneities and heterogeneities in the aerosol field on cloud formation, cloud features, and subsequent precipitation. To achieve this, numerical simulations with the Consortium for Small-scale Modeling (COSMO) model at a convection-permitting horizontal grid spacing will be performed for several cases with different synoptic conditions. By using an advanced two-moment microphysical parameterization, aerosol effects on clouds and precipitation are modeled by taking into account aerosol assumptions for cloud condensation nuclei (CCN). This way, we will identify which inhomogeneity has the strongest influence on convective processes over both flat and complex terrain and to what extent the synoptic forcing modifies the sensitivity of clouds and precipitation. The area of investigation will be Germany where processes over both flat and orographically structured terrain can be studied.
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