Agile Adaptive Radar Sampling of Fast-Evolving Atmospheric Phenomena Guided by Satellite Imagery and Surface Cameras

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) expert judgment or (ii) stand-alone automated identification and tracking algorithms operating on measurements collected by radar. 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.