12B.2 GPM KaPR Deep Convection Observations: Insight for Future Spaceborne Radar Missions

Thursday, 31 August 2023: 8:15 AM
Great Lakes A (Hyatt Regency Minneapolis)
Randy Chase, Colorado State University, Fort Collins, CO; Colorado State Univ., Fort Collins, CO; and B. Dolan, P. Kollias, K. L. Rasmussen, G. L. Stephens, and G. R. Leung

The NASA Ka-band Radar in a CubeSat (RainCube) mission demonstrated the technology readiness of deployable antennas, solid-state power amplifiers with pulse compression and overall system design with lower size, weight and power (SWAP) requirements. Amongst other factors, these advancements have contributed to the spaceborne radar technology used in future missions such as NASA’s INvestigation of Convective UpdraftS (INCUS), as well as private company funded missions like Tomorrow.io, that plan to use Ka-band. While Ka-band is well situated to observe both cloud and precipitation sized particles with its 8-mm wavelength, it suffers from considerably more attenuation from liquid particles and can encounter multiple scattering more frequently than Ku-band. Thus, future missions would benefit from understanding the Ka-band intricacies in order to enhance their overall scientific goals.
To provide insight on what future Ka-band radar systems might encounter, we leverage the Global Precipitation Measurement (GPM) program of record, which contains coincident measurements of Ku- and Ka-band. With more than 9 years of data and millions of measured profiles globally (60N-60S), we quantify some potential challenges of Ka-band. We compute the frequency of multiple scattering events, where they occur globally using Ka alone and the Dual-Frequency Ratio (DFR) and provide a new Ka only machine learning classifier of multiple scattering events. Furthermore, we quantify the attenuation characteristics of Ka-band as a function of echotop in order to anticipate signal loss for future Ka-band missions. Lastly, we analyze the frequency of consistency between the measured and retrieved parameters within NASA’s GPM combined algorithm in order to isolate a high-quality reference dataset that future Ka-band missions can leverage to retrieve profiles of water content or precipitation rate. While the GPM KaPR is not a perfect analog for future missions (e.g., differences in minimum sensitivity, footprint size, viewing angle etc.), we show there is utility in leveraging the GPM KaPR program of record.
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