Abstract
Current GNSS-R (GNSS reflectometry) techniques for sea surface measurements require data collection over longer periods, limiting their usability for real-time applications. In this work, we present a new, alternative GNSS-R approach based on the unscented Kalman filter and the so-called inverse modeling approach. The new method makes use of a mathematical description that relates SNR (signal-to-noise ratio) variations to multipath effects and uses a B-spline formalism to obtain time series of reflector height. The presented algorithm can provide results in real time with a precision that is significantly better than spectral inversion methods and almost comparable to results from inverse modeling in post-processing mode. To verify the performance, the method has been tested at station GTGU at the Onsala Space Observatory, Sweden, and at the station SPBY in Spring Bay, Australia. The RMS (root mean square) error with respect to nearby tide gauge data was found to be 2.0 cm at GTGU and 4.8 cm at SPBY when evaluating the output corresponding to real-time analysis. The method can also be applied in post-processing, resulting in RMS errors of 1.5 cm and 3.3 cm for GTGU and SPBY, respectively. Finally, based on SNR data from GTGU, it is also shown that the Kalman filter approach is able to detect the presence of sea ice with a higher temporal resolution than the previous methods and traditional remote sensing techniques which monitor ice in coastal regions.
Highlights
A significant portion of the world’s population lives and operates in coastal regions and is susceptible to hazards originating from the state of the sea (Neumann et al 2015)
Since it has previously been proved that the GNSS stations can be used directly to monitor sea level (Larson et al 2013) with a technique called GNSS reflectometry (GNSS-R), we look into using this technique to retrieve sea level in real time
GNSS reflectometry works like a passive radar system in that it uses signals that are broadcasted by some other source, in this case the GNSS satellites, and utilize these for measurement purposes other than those intended originally
Summary
A significant portion of the world’s population lives and operates in coastal regions and is susceptible to hazards originating from the state of the sea (Neumann et al 2015). To detect imminent threats such as storm surges and tsunamis, real-time measurements of the current sea level are necessary (Holgate et al 2008). Real-time sea-level reports are necessary for day-to-day operations of, for example, shipping routes and ports (Pugh 2004). For these reasons, the Global Sea Level Observing System (GLOSS) initiative of the Intergovernmental Oceanographic Commission (IOC) of UNESCO encourages the collection and distribution of data in real time or near real time (UNESCO/IOC 2012).
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