TY - JOUR
T1 - Shipborne GNSS reflectometry for monitoring along-track significant wave height and wind speed
AU - Wang, Feng
AU - Yang, Dongkai
AU - Wang, Jiuke
AU - Xing, Jin
AU - Yu, Yongqing
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/8/1
Y1 - 2023/8/1
N2 - Shipborne Global Navigation Satellite System-Reflectometry (GNSS-R) for measuring along-track significant wave height (SWH) and wind speed are first investigated in this article. A real-time shipborne GNSS-R instrument was carried on the XUELONG-2 for the 38th Antarctic Scientific Expedition. The collected dataset covered the entire expedition of the XUELONG-2. The dependencies of the correlation time, normalized area, ratio of reflected-to-direct signal-to-noise ratio (SNR), spectral width, and peak frequency on SWH and wind speed are explored. It is found that the correlation time, spectral width, and peak frequency better depend on SWH and wind speed than other two observables, therefore, they are used to measure the SWH and wind speed. The elevation angle, SNR and ship velocity, also impact on the observables, thus their influences should be corrected. The geophysical model function (GMF) and neural network are used to develop retrieval models of SWH and wind speed. Neural networks provide better retrieval performances. The best root mean square errors (RMSEs) of the retrieved SWH and wind speed are 0.38 m and 2.49 m/s, with a temporal and spatial resolutions of 1 min and 600 m, respectively. The measurements from the shipborne GNSS-R also agree with the co-located European Centre for Medium-Range Weather Forecasts (ECMWF) and Jason-3 altimeter data.
AB - Shipborne Global Navigation Satellite System-Reflectometry (GNSS-R) for measuring along-track significant wave height (SWH) and wind speed are first investigated in this article. A real-time shipborne GNSS-R instrument was carried on the XUELONG-2 for the 38th Antarctic Scientific Expedition. The collected dataset covered the entire expedition of the XUELONG-2. The dependencies of the correlation time, normalized area, ratio of reflected-to-direct signal-to-noise ratio (SNR), spectral width, and peak frequency on SWH and wind speed are explored. It is found that the correlation time, spectral width, and peak frequency better depend on SWH and wind speed than other two observables, therefore, they are used to measure the SWH and wind speed. The elevation angle, SNR and ship velocity, also impact on the observables, thus their influences should be corrected. The geophysical model function (GMF) and neural network are used to develop retrieval models of SWH and wind speed. Neural networks provide better retrieval performances. The best root mean square errors (RMSEs) of the retrieved SWH and wind speed are 0.38 m and 2.49 m/s, with a temporal and spatial resolutions of 1 min and 600 m, respectively. The measurements from the shipborne GNSS-R also agree with the co-located European Centre for Medium-Range Weather Forecasts (ECMWF) and Jason-3 altimeter data.
KW - Geophysical model function (GMF)
KW - Neural network (NN)
KW - Shipborne GNSS reflectometry
KW - Significant wave height (SWH)
KW - Wind speed
UR - https://www.scopus.com/pages/publications/85163346309
U2 - 10.1016/j.oceaneng.2023.114935
DO - 10.1016/j.oceaneng.2023.114935
M3 - 文章
AN - SCOPUS:85163346309
SN - 0029-8018
VL - 281
JO - Ocean Engineering
JF - Ocean Engineering
M1 - 114935
ER -