OPTIMice-close: Optimal combination of Polarimetric and Triple frequency radar techniques for Improving Microphysical process understanding of cold clouds and associated rainfall

• Applicant: Dr. Stefan Kneifel
• Subject Area: Atmospheric Science
• Term: from 2016 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 290611444

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. 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.Finally, we will 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.

• DFG Programme: Independent Junior Research Groups