VCHILL 2: A System for Semantic Processing of Radar Data
This system is based around a series of interconnected components leveraging geospatial databases, HTML5 web servers, and a RESTful interface implemented in python. The system currently contains over 14 years worth of data from the CSU-CHILL radar, however the backend implementation accepts multiple different radar formats from a wide variety of instruments including NEXRAD, IRIS, and UF based systems.
This system allows for higher-level search functionality than is currently possible with operational systems. An example query we empower is a user requesting all tornado possible days, defined as days with at least 30 % of the scan having 30 dbz or higher, and maximum measured velocities of 40 m/s or higher. Another example would be requesting flood prevalent conditions defined as days with rainfall coverage in at least 50% of the scan, and maximum rain rates of greater than 10 mm/hr.
These are just a few possible examples and the user is in full control of how they want to define their criterion. The backend design is very modular and it is very easy to add new types of data. Plugins just need to accept a radar common data model object that is shared between multiple python libraries(pyart, pydisdrometer, etc.).
This paper will detail both the implementation of our system, as well as discussing the new types of workflows this methodology allows.