Next generation of Intelligent Meteorological Radar with Built-in Understanding of the Scenery (NIMBUS) (NIMBUS)

We propose the development of NIMBUS (Next generation Intelligent Meteorological Radar with a Built-in Understanding of the Scenery), an ultra-compact multi-frequency Ka/W/G-band radar system with onboard intelligence. The radar instrument leverages three key technologies to reduce the size, weight and power and provide autonomous agility to enable observation of a broad range of atmospheric phenomena: (1) Radar-on-a-chip ultra-compact architecture, integrating millimeter-wave electronics into silicon RFICs, a technology commonly used in the automotive industry and now applied for the first time in weather focused mission concept. (2) RF photonics source recently developed and demonstrated as part of NASA/ESTO ACT-20. The RF photonics source with world-record low phase noise is critical for pulse-compression techniques, enabling the reduction of range-sidelobes caused by surface clutter. (3) Onboard intelligence powered by machine learning algorithms, programmed on a Snapdragon digital processor platform. This capability enables real-time adaptation of radar operations to the various cloud and precipitation scenarios which result in a range of diverse measurement requirements. This AI-driven approach allows targeted use of radar power for the specific measurements of interest by optimizing waveform choices dynamically to achieve the best balance of sensitivity and resolution at each of the operating wavelengths while operating within the bounds of a low-power small platform. Drawing insights from look-ahead multi-frequency passive sensors, and contextual factors such as location and environmental parameters, this continuous learning process enables the system to adapt and refine its strategies depending on the scenario being observed. NIMBUS builds upon the successful demonstration of IIP-19/CloudCube compact radar architecture (Ka-/W-/G-band, with direct heritage from RainCube), as well as complementary advances achieved under the IIP-19/SMICES, making another significant leap into the miniaturization of radar instruments while achieving a performance comparable to larger radars by adding operational agility. As part of this IIP effort, we will build and test the ultra-compact radar prototype and conduct initial airborne demonstrations. We will also simulate and validate machine learning algorithms with pre-launch configurations and airborne operations to ensure their effectiveness and adaptability in real atmospheric scenarios. The spaceborne instrument has an entry level TRL of 3 and a planned exit of TRL 4 (or airborne TRL-6) over three years of performance. The NIMBUS instrument represents a significant departure from traditional radar approaches, transitioning towards AI-driven methodologies. Unlike conventional systems reliant on fixed settings and manual adjustments, NIMBUS dynamically adapts its operations to the atmospheric scenery enabling adaptive radar sampling that minimizes instrument power draw without compromising performance. This innovation will enable unprecedented mission concepts that would fill existing gaps in the observation of a variety of cloud and precipitation processes. Missions will include, but not be limited to, low-cost radar options relevant to architectures compatible with small spacecraft platforms targeting observables such as the cloud, convection and precipitation (CCP), global monitoring of atmospheric winds, and observations of critical elements of the Planetary Boundary Layer (PBL). NIMBUS will also provide a flexible instrument capability to complement other instruments (e.g. lidar, spectrometer, or microwave radiometer) in larger mission concepts targeting the same observables.

Increase scientific understanding of natural phenomena using remote sensing.

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