Speaker
Description
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 of opportunistic measurements, which call for tailored machine-learning tools, and partly to the wide variability in data quality across sensors.
Here, we present a data-driven fusion method combining radar, gauges, and opportunistic measurements within a multimodal transformer framework. The model explicitly represents sensor behaviour and data reliability, allowing heterogeneous observations to be integrated in a consistent way. We assess its performance through quantitative and qualitative comparisons with the French operational fusion algorithm ANTILOPE.