Hail retrievals for the open source community#
Hail is a global hazard, with damage to property, vehicles and agriculture resulting in billions of dollars in losses every year. This has motivated decades of research into estimating the size and intensity of hail from weather radar, and now numerous algorithms exist that work on operational weather radar datasets. PyHail represents a single library where many of these retrievals are available to enable rapid integration into existing Py-ART projects.
Hail retrievals provide an estimate of hail severity, including maximum dimension, accumulation depth and kinetic energy. These estimates can be used to:
Produce nowcasts of hail fall using tools like TITAN, TINT or PySTEPS
Produce case studies of recent events to map hail fall locations
Verify the performance of NWP parameters
Produce long-term estimates of hail frequency using radar data, which can be used to train or validate environmental parameters
We look forward to the seeing more widespread use of hail retrievals using pyhail and the exciting new research and outputs this will bring. If you have any comments or issues, please get in contact via the pyhail issues page on github.