4.7 Article

The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3087426

Keywords

Wind speed; Global Positioning System; Sea surface; Wind forecasting; Space vehicles; Predictive models; Calibration; Geophysical measurements; global positioning system; microwave reflectometry; radar measurements; remote sensing; scattering; sea surface; wind

Funding

  1. NASA Cyclone Global Navigation Satellite System (CYGNSS) project (NASA) [17-CYGNSS17-0027]

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A novel method for addressing calibration issues in the Cyclone Global Navigation Satellite System has been proposed using track-wise $sigma<^>o$ bias correction to improve signal sensitivity and remove calibration issues. By utilizing this method with corrected measurements, a wind retrieval approach was evaluated against various weather forecasting models and instruments, showing consistency and reliability for operational use.
A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise $sigma<^>o$ bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise $sigma<^>o$ bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term $sigma<^>o$ downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected $sigma<^>o$ measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are -0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are -0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are -0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are -0.54 and 2.90 m/s, and -4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

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