Second International Conference on Opportunistic Sensing of Precipitation - OpenSense 2026

Session

Oral session #5

Jun 24, 2026, 1:45 PM

Building A (Royal Netherlands Meteorological Institute)

4. End-to-end data-driven fusion of radar, gauge, and opportunistic sensors for rainfall estimation

Dr Pierre Lepetit (Météo-France)

6/24/26, 1:45 PM

OS data merging

Fusion of radar and rain gauge measurements with opportunistic ground data, such as personal weather station rain rates and commercial microwave link attenuations, remains an open challenge. While machine learning offers promising perspectives in this area, end-to-end data fusion approaches remain an open research issue. This is partly due to the irregular temporal and spatial characteristics...

39. CML rainfall field reconstruction with GenAI and a combination with geostationary satellite data

Selina Janner (KIT/IMK-IFU)

6/24/26, 2:00 PM

OS data merging

Commercial Microwave Links (CMLs) provide path-integrated rainfall estimates, but their irregular geometry poses a substantial challenge for deriving gridded rainfall maps.

In this contribution, we propose a generative AI approach for reconstructing rainfall fields from CML-derived rainfall estimates. The central idea is to learn characteristic precipitation structures from reference radar...