Wind Direction Retrieval Using Spaceborne GNSS-R in Nonspecular Geometry

Abstract

A wind direction retrieval algorithm using spaceborne global navigation satellite system reflectometry (GNSS-R) in nonspecular geometry is presented. This article first focuses on investigating the influence of wind direction on the scattering strength purely in specular geometry and nonspecular geometry. The sensitivity of the scattered GNSS signals in nonspecular geometry to wind direction is demonstrated despite the weaker power level. Therefore, a wind direction retrieval algorithm based on the geophysical model function (GMF) whose parameters are parameterized as a look-up-table is proposed. Then, the spatial resolution and power link of the spaceborne GNSS-R in nonspecular geometry are analyzed. The incidence angle of the scattered signals ranges 15 • to 35 • to ensure the sensitivity to wind direction and the power level of scattered signals. The retrieving results using simulated data show that the ambiguity of the measurement geometry can be eliminated by using the multisatellite observation, and two possible wind directions for the same corresponding observable are obtained in the wind direction retrieval algorithm. The accuracy of wind speed importantly impacts on the retrieving performance of wind direction, therefore, it is needed to improve the measuring accuracy of wind speed. These results provide information to guide the wind direction retrieval using spaceborne GNSS-R in nonspecular geometry.

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

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Assimilation of GNSS-R Delay-Doppler Maps into Weather Models

“…Although it requires a more complicated Jacobian, using a bi-directional slope distribution is possible to obtain wind direction information from the GNSS-R observations, especially at areas away from the specular point [124,125]. It is also more suitable for the data assimilation since the state of NWP models are wind vector components.…”

A Real-Time GNSS-R System for Monitoring Sea Surface Wind Speed and Significant Wave Height

“…The GNSS-R technology corrects the flaws in traditional remote sensing technologies and has significant research value. Because the reflected signals differ from the signal directly received from the satellite in terms of delay, Doppler shift, power strength, and polarization, they can be used to characterize the Earth’s surface [ 27 , 28 , 29 , 30 , 31 , 32 ]. These differences are dependent on the scattering surface’s geophysical qualities, and so may contain information about the surface geophysics.…”