Agile adaptive radar sampling of fast-evolving atmospheric phenomena
guided by satellite imagery and surface cameras
Abstract
The collection of high temporal resolution radar observations without
compromising data quality requires adaptability and agility. So far,
radar beam steering has been mostly guided by i) the expert judgment or
ii) stand-alone automated identification and tracking algorithms
operating on measurements collected by the radar itself. The current
study proposes a new paradigm, where external observations are used to
optimize a radar’s sampling strategy. Here the sampling strategy of a
phased-array radar and a polarimetric scanning cloud radar, two
different yet uniquely complementary systems, is guided by an algorithm
that uses observations from a geostationary satellite, a surface camera
and the radars themselves to identify and track atmospheric phenomena.
The tailored pointing and increase in sensitivity realized through this
framework enables the steered radars to sample a diverse set of
atmospheric phenomena such as shallow cumuli, lightning-induced ice
crystal orientation and a series of waterspouts.