Abstract
In this article, a retrieval algorithm based on the use of an artificial neural network (ANN) is proposed for wind speed estimations from cyclone global navigation satellite system (CYGNSS). The delay/Doppler map average and the leading edge slope observables, derived from CYGNSS delay/Doppler maps, are used as inputs to the network, along with geographical, geometry, and hardware antenna information. The derivation of the optimal number of hidden layers and neurons is obtained using statistical metrics of agreement between the CYGNSS data and the wind matchups obtained from modelled winds output by the wavewatch 3 (WW3) model. A cumulative distribution function (CDF) matching step is applied to the network outputs, to impose that the CDF of the retrievals matches that of the matchups. The resulting wind speeds are unbiased with respect to WW3 modeled winds, and deliver a global root mean square (RMS) difference (RMSD) of 1.51 m/s, over a dynamic range of wind speeds up to 32 m/s. The obtained RMSD is the lowest among those seen in literature for wind speed retrievals from CYGNSS. A comparison is carried out between the winds retrieved from the ANN approach and those derived using the fully developed sea approach, which represent the CYGNSS baseline wind product. The comparison highlights that the ANN approach outperforms the baseline approach for both low and high wind speeds and removes most of the geographical biases between baseline winds and WW3 winds seen in monthly maps of wind speeds. The ANN approach could well be applied to the entire CYGNSS dataset to generate an enhanced wind speed product.
Highlights
G LOBAL navigation satellite system-reflectometry (GNSS-R) is a passive remote sensing technique that uses global navigation satellite signals reflected off of the Earth’s surface to gain information about the characteristics of those surfaces
This study demonstrates that a significant improvement in the wind speed estimation can be globally achieved through the use of an Artificial Neural Network (ANN) approach, providing global estimates of wind speeds which are better than the baseline ones over the full dynamic range of wind speeds
The Fully Developed Seas (FDS) baseline winds were obtained from a geophysical model functions (GMFs) trained on winds from the Global Data Assimilation System (GDAS), which are slightly different from wavewatch 3 (WW3) winds
Summary
G LOBAL navigation satellite system-reflectometry (GNSS-R) is a passive remote sensing technique that uses global navigation satellite signals reflected off of the Earth’s surface to gain information about the characteristics of those surfaces. These retrievals are achieved using the so-called baseline approach that implements the minimum variance combination of wind estimates from two observables, derived from CYGNSS delay/Doppler maps, and known as delay/Doppler map average (DDMA) and leading edge slope (LES) [6] [8]. This study demonstrates that a significant improvement in the wind speed estimation can be globally achieved through the use of an Artificial Neural Network (ANN) approach, providing global estimates of wind speeds which are better than the baseline ones over the full dynamic range of wind speeds
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