Abstract
A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> 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.
Highlights
O N DECEMBER 15, 2016, the Cyclone Global Navigation Satellite System (CyGNSS) mission, a constellation of eight low Earth-orbiting small satellites, was launched into space
Each spacecraft registers approximately 1000–1100 tracks per day. Along each of these tracks, the CyGNSS normalized bistatic radar cross section (NBRCS) has been reported at a 1-Hz sampling rate, resulting in approximately ∼6-km spacing between adjacent NBRCS measurements
1) CyGNSS signalto-noise ratio (SNR) Variability: While developing our trackwise-based wind retrieval algorithm, we discovered that the quality of the σ o decreases substantially as the Rx gain decreases below ∼3–4 dB
Summary
O N DECEMBER 15, 2016, the Cyclone Global Navigation Satellite System (CyGNSS) mission, a constellation of eight low Earth-orbiting small satellites, was launched into space (see Fig. 1). In [5], the NBRCS calibration from the v2.0 dataset was carefully assessed where the presence of intersatellite NBRCS biases was observed; a dependence between NBRCS and instrument noise floor (NF) was identified and believed to be the main drive for these biases This issue was reported to CyGNSS scientists and engineers, and a temporary solution for this problem was provided and implemented in the version 2.1 release [6]. Despite the above-mentioned calibration improvements, we have reported a ∼.5-dB decrease in v2.1 NBRCS between May 2017 and January 2019 across all satellites This issue resulted in a nonnegligible increase in the wind speed bias between CyGNSS and numerical weather prediction (NWP) models across the same time period (i.e., ∼+2 m/s) [9].
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