A G-band Radar for Humidity and Cloud Remote Sensing

Ken Cooper, Richard Roy, Robert Dengler, Raquel Rodriguez Monje, Maria Alonso-delPino, Jose Siles, Okan Yurduseven, Parashare Chaitali, Luis Millan, Lebsock Matthew

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Abstract

VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200-300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167-174.8 GHz results in a nominal range resolution of 15 m. The radar's measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as -26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR's absolute sensitivity was validated using a spherical metal target in the radar antenna's far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver.
Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date04 Jun 2020
DOIs
Publication statusEarly online date - 04 Jun 2020

Keywords

  • radar
  • millimeter wave

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    Cooper, K., Roy, R., Dengler, R., Rodriguez Monje, R., Alonso-delPino, M., Siles, J., Yurduseven, O., Chaitali, P., Millan, L., & Matthew, L. (2020). A G-band Radar for Humidity and Cloud Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2020.2995325