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 aims 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 aim to enhance the precision of precipitation estimates.
We invite all data science and machine learning enthusiasts to join us for this exciting challenge in a relaxed, collaborative environment. Participants will enhance their skills in data analysis, machine learning, and meteorological modeling. No prior experience with weather data is required. Additionally, you will have the opportunity to connect with like-minded individuals and work with KIT´s supercomputer cluster.
This event is organized by machine learning enthusiasts from KCDS, with support from MathSEE, Triangle and SCC.
