Smart Ice Cloud Sensing

We propose an instrument optimized for Smart Ice Cloud Sensing (SMICES) that will enable both multi-angle and multi-resolution measurements of cloud ice particles size and shape within the tropospheric temperature and water vapor profile context. This will improve understanding of tropospheric events including hurricanes, tropical deep convections, tornadoes and storms. SMICE will combine an active radar, with passive multi-band radiometers and sounders using an intelligent backend and control system. SMICES will maximize the scientific outcome by performing multi-angle and multi-resolution measurements of interesting tropospheric features and optimizing the volume of collected data by using a footprint overlap high resolution mode for the radiometers, a track-and-lock algorithm for the radar, and a reconfigurable wide-band high-resolution digital spectrometer to produce sounder channel spectra. These three system characteristics enable feature-dependent and incidence angle-dependent resolutions resulting in efficient acquisition of high resolution measurements. The intelligent feature detection enabled by SMICES, including observational locking capability and the multi-angle and multi-resolution measurements, will enable the detection of ice particles at different sizes, distribution and granularity with a fine vertical resolution of 500 meters. The use of the spectrometers enable collocated temperature and water vapor profiles measurements needed for evaluating and constraining climate model simulations of ice cloud processes. The SMICES instrument works in the following manner. During normal operation, the radiometers continuously scan the upper troposphere at 45° incidence angle. Passive instrument calibration will be performed on board, allowing for near real-time detection of tropospheric features. Once the passive sensors detect a tropospheric feature, the radar, which is nominally nadir pointed, will point towards the feature and examine the region of interest. As the satellite travels, the relative position between the feature and the instrument also changes. By locking the radar to the target, the radar will be able to obtain multi-angle data. This intelligent control of the radar enables high-resolution data for specific features of interest. Along with combination of active and passive sensing techniques, the "smart" functionality of SMICES is a key feature. This is enabled by calibration and feature detection algorithms using neural networks. The radiometric calibration and feature detection neural networks operation will be performed on-orbit. Antenna temperature will be estimated from the radiometric voltage reading and system operating condition using a multi-layer deep-learning neural network. The calibration neural network will have the capability to on-orbit training to account for non-stationary system effects including component aging. The feature detection neural network operation will be trained on ground using multiple datasets, and can be updated to the spacecraft as necessary. SMICES relies on significant technology to meet its mission goals. The calibration algorithm and feature detection is currently rated to be TRL 3, as well as the antenna focus and tracking. The high resolution digital spectrometer is currently TRL 4. The Sounders and radiometers operate at 240, 310, 380, 670 and GHz and are estimated to be at TRL 5 from IIP-13 TWICE and ACT-17 IRaST. Exit TRL level of the SMICES instrument will be TRL-5. TRL4 radar receiver and up-converter operate at 233 GHz and have been developed on the DARPA ViSAR program. The 233 GHz TWT will leverage work from the DARPA ViSAR program, with the magnet being changed from a fixed magnet to a PPM. We therefore rank the TWT as TRL3.

Increase scientific understanding of natural phenomena using remote sensing

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