RainQuest: Precipitation Estimation from Weather Radar Data

Accurately estimating rainfall by radar data is challenging because radars measure reflectivity rather than direct rainfall, and environmental variations further complicate this conversion. The RainQuest hackathon aimed to address this problem by developing models that integrate precise point measurements from rain gauge data with radar reflectivity, which offers better measurements resolution. By combining these data sources, we aimed to enhance the precision of precipitation estimates.

We invited all data science and machine learning enthusiasts to join us for this exciting challenge in a relaxed, collaborative environment. Participants enhanced their skills in data analysis, machine learning, and meteorological modeling. Additionally, they had the opportunity to connect with like-minded individuals and work with KIT´s supercomputer cluster. 

This event was organized by machine learning enthusiasts from KCDS, with support from MathSEE, TRIANGEL and SCC.

Thanks to all contributors and participants for a great hackathon!