Optimal combination of Polarimetric and Triple frequency radar techniques for Improving Microphysical process understanding of cold clouds
What is our scientific motivation?
Clouds and precipitation are still one of the biggest challenges for weather prediction and climate models. The latest IPCC report points out that “(…) especially the microphysical processes in clouds comprised by mixtures of ice and liquid water are poorly understood (…)” which in turn hampers their proper modelling. These mixed-phase clouds are frequently observed at high latitudes but also at mid-latitudes most clouds are comprised by areas with temperatures below freezing and also most of the precipitation in mid-latitudes is produced via the ice phase.
In order to make progress in a better understanding of how ice particles nucleate, how they grow to larger ice particles, snowflakes, or graupel, we need comprehensive and synergistic observations to validate and further develop model parametrizations of these processes.
What is the Emmy-Noether Young Researcher Group OPTIMice?
The German Research Foundation (DFG) supports young scientists in their early career stage with the so called Emmy-Noether program to build their own small research group with an innovative scientific topic of their choice. The project duration is 5 years and also includes funding for new instrumentation. The OPTIMIce Emmy-Noether group has started in 2017 at the University of Cologne in close collaboration with many partners including the University of Bonn and the Research Center Jülich.
We are always interested in new collaborations related to our research topics! Especially if you are an early career scientists from Germany or abroad and you would like to work with our group as a visiting scientist or doing a Bachelor or Master study with us, please contact the group leader Dr. Stefan Kneifel. Funding opportunities for visitors from abroad are provided by several German institutions such as the German Academic Exchange Service.
What is our scientific focus and goal?
Within this project we aim to combine state-of-the-art remote sensing techniques i.e. scanning polarimetric radar, triple-frequency radar, radar Doppler spectra with novel in-situ sensors and passive remote sensors. Such a sensor combination is necessary because with a single sensor it is impossible to entangle all components of the underlying complex cloud processes. The combination of new instruments with existing infrastructure will provide us a powerful tool to target these processes in unprecedented detail.
Remote sensing observation are always indirect measurements (e.g. radar reflectivity) of the quantities predicted by a numerical cloud model (e.g. ice water content). Therefore, we will build a radiative transfer framework which allows to simulate the observations based on model output. In this way real and synthetic observations can be directly compared. A central goal of the new radiative transfer framework will be to better characterize the scattering properties of frozen particles. For this we will perform numerical scattering calculations but also collect existing scattering datasets into an open-access scattering library. Another goal is to actively organize workshops on this topic and to provide platforms that will help to build a snow scattering community, foster communication and collaboration between the different groups and to find common standards for e.g. storing the complex scattering simulation results.
Finally, we aim to apply this framework to long-term simulations from the german weather forecast model but also to detailed microphysical simulations from a 1D spectral model. With the 1D model we aim to identify weaknesses in current process understanding by simulating case studies where the observations reveal a specific microphysical fingerprint e.g. by riming of ice particles within a layer of super-cooled liquid water. The results from this studies will be of great value also for the improvement of the less-detailed parametrizations used in numerical weather prediction and climate models.
Within this project we aim to provide not only a completely novel observational dataset but also new strategies how an optimal knowledge transfer from observations to an improved modelling of cold cloud microphysical processes can be best achieved.