My research employs in-situ observations together with radar–lidar measurements from aircraft, surface-, and satellite-based field campaigns to investigate cloud microphysical properties like size parameters, water and ice concentration, etc., through statistical analyses. These insights contribute to improving global climate model predictions, where inaccurate representations of cloud structure and microphysics often lead to significant biases.

A major component of my work focuses on developing simplified cloud microphysical retrieval algorithms and integrating multi-platform observational datasets from diverse instruments. This approach reduces biases in the cloud detection capabilities of remote sensors and supports the advancement of atmospheric observing systems and future field campaigns.