Research fields:

• Rainfall observation with opportunistic sensors (CML, SML and PWS)
• Merging weather radar and ground-based sensors
• Deep Learning methods for quantitative precipitaiton estimation
• Deep Learning for post-processing atmospheric model output

Current projects as PI:

• MERGOSAT - Merging of rain rate estimates from opportunistic sensors and geostationary satellites
• RealPEP - Near-Realtime Quantitative Precipitation Estimation and Prediction (project website)

Other current projects:

• HoWa-PRO - Innovative methods of precipitation measurement and forecasting in use for early flood warning in small catchments (project website)
• OpenSense COST Action -  Opportunistic Precipitation Sensing Network (project website)

Former projects as PI:

• RESEAD - Robust Environmental Sensor data using Explainable data-driven Anomaly Detection
• SpraiLINK - Spatial rainfall estimates using improved observations from commercial microwave links and statistical data fusion