Leveraging FAIR principles for efficient management of meteorological radar data

Radars are crucial in meteorology for their precise spatio-temporal resolution, enabling early detection and tracking of severe weather. This capability aids meteorologists in issuing timely alerts, thus safeguarding lives and reducing property damage. Radar data also supports offline applications like cloud and precipitation analysis, climatology, and insurance risk assessment, all relying on its time-series nature. However, storing radar data traditionally involves proprietary formats with high I/O demands, leading to slow computations and resource-intensive requirements.

To address these challenges, a new data model is proposed using the CF format-based FM301 hierarchical tree structure and ARCO formats. This model efficiently organizes radar data into cloud-storage buckets using Python libraries like Xarray, Xradar, Wradlib, and Zarr. Demonstrated with Carimagua, Colombia radar data, the model shows faster processing times than legacy methods on standard hardware. Emphasizing FAIR principles (Findable, Accessible, Interoperable, Reusable), this approach enhances accessibility to radar data on cloud platforms, promoting open science and wider societal benefit.