  We propose an innovative meteorological radar, which uses reduced number of
spatiotemporal samples without compromising the accuracy of target information.
Our approach extends recent research on compressed sensing (CS) for radar
remote sensing of hard point scatterers to volumetric targets. The previously
published CS-based radar techniques are not applicable for sampling weather
since the precipitation echoes lack sparsity in both range-time and Doppler
domains. We propose an alternative approach by adopting the latest advances in
matrix completion algorithms to demonstrate the sparse sensing of weather
echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and
illustrate our algorithms.
