NASA’s Investigations of Convective Updrafts (INCUS) mission aims to document convective updraft mass flux through changes in the radar reflectivity (ΔZ) in convective cores captured by a constellation of three Ka-band radars sampling the same convective cells over intervals of 30, 90 and 120 s. Here, high spatiotemporal resolution observations of convective cores from surface-based radars that use agile sampling techniques are used to evaluate aspects of the INCUS measurement approach using real observations. Analysis of several convective cells confirms that large coherent ΔZ structure with measurable signal (> 5 dB) can occur in less than 30 s and are correlated with underlying convective motions. The analysis indicates that the INCUS mission radar footprint and along track sampling are adequate to capture most of the desirable ΔZ signals. This unique demonstration of reflectivity time-lapse provides the framework for estimating convective mass flux independent from Doppler techniques with future radar observations.

Measurements of in-cloud vertical air motion are key to quantitatively describe cloud dynamics and their role in cloud microphysics. Here, a retrieval technique for estimating the in-cloud vertical air motion using the upward edge of the radar Doppler spectrum is presented. An additional broadening correction factor that depends on the signal-to-noise ratio (SNR) is introduced. A variety of independent measurements are used to assess the performance of the new retrieval. The vertical air motion is unbiased with an uncertainty of 0.2 ms-1for SNR less than 30. The properties of in-cloud vertical air motion are investigated from one-year of ground-based observations of warm marine boundary layer clouds. Clouds with higher LWP are characterized by stronger vertical air motions compared to those having lower LWP values.

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.