Tuesday, 24 January 2017: 11:15 AM
Conference Center: Chelan 5 (Washington State Convention Center )
Severe weather research often is facilitated by multi-Doppler radar syntheses to retrieve three-dimensional winds. While numerous techniques and software packages to perform these analyses have been developed over the years, to date none has been fully integrated within the Python software framework that has been growing in popularity within the weather radar science community. This presentation will discuss work that has been done to merge the three-dimensional variational analysis (3DVAR) approach developed at the University of Oklahoma with the Python Atmospheric Radiation Measurement Radar Toolkit (Py-ART) developed at Argonne National Laboratory. National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) has successfully produced a working Python-based prototype that is being released to the community at large. The package, called MultiDop, is fully compatible with Py-ART but also takes advantage of the computational efficiency supplied by the original 3DVAR codebase. Example workflows will be described using Jupyter notebooks that ingest native spherical coordinate radar volumes, provide quality control, and then merge the data onto a common Cartesian grid and perform multiple-Doppler syntheses, with final products matching the Py-ART data model. Performance against other multi-Doppler packages (e.g., Custom Editing and Display of Reduced Information in Cartesian Space, or CEDRIC) will be assessed. The MultiDop package is intended for community use and community-directed future development via the NASA GitHub portal.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner