In-situ observation and sampling of significant weather phenomena is meaningful, but often it is meteorologically uninformative given significant limits to positioning observers and instrumentation near what are often dangerous atmospheric conditions. Weather radar data can offer rather detailed and dynamic characterizations of phenomena for the bulk of weather phenomena that are of greatest interest. The full scientific potential of radar data is not normally realized simply because current radar visualization is rather basic – typically on a flat screen on which, at best, static 3-D representations are rendered. Almost universally, radar data displays revert to 2-D upon making them dynamic by adding the dimension of time. For many atmospheric phenomena this is adequate, and it is currently the only option for real-time operational analysis. However, improving our understanding of the underlying physics of atmospheric phenomena and providing instruction in the area of atmospheric dynamics could benefit immensely from unlocking the power of the big data stemming from radar that are only modestly portrayed using today’s common display tools.
Bill Carstensen, David Carroll, Drew Ellis, Peter Sforza
Architecture & Urban Studies
Natural Resources & Environment