Abstract
This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated CYGNSS Level 1B (version 2.1) observables and the wind speed from European Centre for Medium-range Weather Forecast Reanalysis 5th Generation (ECMWF ERA5) data in coastal regions. An ANN model with five hidden layers and 200 neurons in each layer has been constructed and applied to the validation set for wind speed retrieval. The proposed ANN model achieves good wind speed retrieval performance in coastal regions with a bias of −0.03 m/s and a RMSE of 1.58 m/s, corresponding to an improvement of 24.4% compared to the CYGNSS Level 2 (version 2.1) wind speed product. The ANN based retrievals are also compared to the ground truth measurements from the National Data Buoy Center (NDBC) buoys, which shows a bias of −0.44 m/s and a RMSE of 1.86 m/s. Moreover, the sensitivities of the wind speed retrieval performance to different input parameters have been analyzed. Among others, the geolocation of the specular point and the swell height can provide significant contribution to the wind speed retrieval, which can provide useful reference for more generic GNSS-R wind speed retrieval algorithms in coastal regions.
Highlights
Sea surface wind is an essential variable in both marine environment monitoring and climate change study (Huang et al, 2003; Peng and Jin, 2019)
Such improvement is expected as the Cyclone GNSS (CYGNSS) Level 2 (L2) v2.1 product is generated with a global geophysical model function (GMF) while the Artificial Neural Network (ANN) wind speed retrieval model function is trained by using the geographical information as the addi tional parameters
The CYGNSS standard Level 2 wind speed product shows a significant performance degradation in coastal region (25–200 km away from the coastline) with respected to in open ocean (RMSE 2.10 m/s vs. 1.88 m/s according to the data in 2018)
Summary
Sea surface wind is an essential variable in both marine environment monitoring and climate change study (Huang et al, 2003; Peng and Jin, 2019). Several studies have shown that ANNs can improve the accuracy of GNSS-R wind speed retrieval using groundbased and spaceborne data (Kasantikul et al, 2018; Liu et al, 2019; Gao et al, 2019a; Asgarimehr et al, 2019), which have shown prom ising performance by using data collected by the TDS-1 (Wang et al, 2018; Asgarimehr et al, 2019) and CYGNSS missions (Liu et al, 2019; Reynolds et al, 2020) This approach has been attempted in some other GNSS-R applications, such as sea ice detection (Yan and Huang, 2018), soil moisture (Feng et al, 2018; Eroglu et al, 2019), hurricane tracking (Alshaye et al, 2020), and inland water detection (Ghasemigoudarzi et al, 2020).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
