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Signal Processing Techniques
Published in Petr Vaníček, Nikolaos T. Christou, GEOID and Its GEOPHYSICAL INTERPRETATIONS, 2020
The particular Kalman filtering/smoothing approach presented above has two disadvantages:(1) the geoid correlation structure must be a third-order Markov process with parameters fixed while running the filter, and (2) the noise model is perhaps oversimplified (white noise). However, these issues may be addressed in a straightforward way, by using the same equations as above, with improved underlying models. West21 and others discussed an adaptive approach in which the two parameters of the geoid correlation model are estimated empirically from autocorrelations computed from the raw data before filtering. This is the simplest way of adapting the filter to each track to be analyzed. Tapley et al.22 used an adaptive first-order Markov model and an extended Kalman filter to model the residual sea surface height for GEOS-3. In that approach, a geoid profile based on a global model is first subtracted from the data. After this subtraction and the usual environmental corrections, the residuals are assumed to be samples from a first-order Markov process. The correlation p.irameter in the Markov model was adaptively adjusted during the filtering process (using an extended Kalman filter) to conform to the changing local nature of ocean surface variations.22
Bathymetry: Assessment
Published in Yeqiao Wang, Coastal and Marine Environments, 2020
Heidi M. Dierssen, Albert E. Theberge
The presence of bathymetric features, such as ridges and troughs, create changes in Earth’s gravity field that produce small fluctuations in the height of the sea surface. The sea surface bulges slightly upward in response to a seamount, for example, as water is attracted to the greater mass (Figure 17.5). Satellite-mounted radar altimeters orbiting the Earth can measure these slight variations in the sea surface-height by sending out radio wave pulses at high frequencies, usually in the range of 13 GHz, for determining sea-surface height variations. The radar pulse scatters off of the sea surface and the round-trip travel time of the signal is measured. If the satellite’s position in orbit is well-characterized, then the round-trip travel time of the pulse can be related quite precisely to the height of the sea surface relative to the satellite sensor and can be estimated to within a few centimeters (Figure 17.5). At wavelengths from 1–200 km, gravity anomaly variations are highly correlated with seafloor topography and have been used to gravimetrically map bathymetry of the world oceans with a spatial resolution of ~10 km resolving features ~20 km in scale.[18]
Ocean Hydrodynamics
Published in Victor Raizer, Optical Remote Sensing of Ocean Hydrodynamics, 2019
Satellite altimeter provides precise measurements of the sea surface height. Surface currents are detectable by variations of sea surface slopes. Small-scale surface features—like small eddies, generated by the large-scale currents (e.g., by the Gulf Stream) can be identified by altimeter data as well. Altimeter data are also used for tide modeling. Launched in 1992, TOPEX/Poseidon altimeters provide global data acquisitions which are employed for mapping basin-wide current variations and validation of OGCMs. The ocean surface currents also can be retrieved using Doppler anomaly from Sentinel-1 SAR.
Creation of a global tide analysis dataset: Application of NEMO and an offline objective analysis scheme
Published in Journal of Operational Oceanography, 2021
David Byrne, Jeff Polton, Colin Bell
The dataset discussed in this study is generated by combining harmonic output from a numerical model and observations using data assimilation techniques. The order of operations used to do this is as follows: Run a numerical global ocean model to obtain fields of sea surface height (SSH).Analyse model output to obtain fields of harmonic amplitudes and phases.Select and analyse observations to obtain harmonic amplitudes and phases.Combine the observed harmonics and model harmonics using an ensemble optimal interpolation scheme (OI).
Geometric stability of stationary Euler flows
Published in Geophysical & Astrophysical Fluid Dynamics, 2020
The pressure in an incompressible ideal flow is related to the velocity by a Poisson equation (Majda and Bertozzi 2002). Compared with velocity vector, it is easier to obtain accurate measurements of a scalar property. In a shallow-water ocean, surface pressure is represented by sea surface height which can be readily measured by satellite altimeter. It is therefore appealing to use pressure as the target proxy for geometric analysis. The idea, however, is not workable because different flows may share the same isobaric topology. For example, the pressure field of parabolic flow S11 follows a straightline distribution, which is geometrically the same as the pressure fields of straightline jet S8 and elliptic vortex S10.
A Decomposition of Total Variation Depth for Understanding Functional Outliers
Published in Technometrics, 2019
Sea surface height plays an important role in understanding ocean currents. Oceanographers run simulations with different initial conditions to generate ensembles. To understand the variability in these model runs, we apply our method to 50 ensemble runs of simulated sea surface heights of the Red Sea on January 1, 2016, where each observation is an image with 25,808 valid values. We first order the observations along the axis of the Red Sea in a zigzag fashion and obtain the one-dimensional functional data as shown in Figure 6. Then, we apply our outlier detection method to the constructed functional data and get two detected shape outliers, one of which is shown in Figure 8(b). The associated MSS values are illustrated in Figure 7. After removing the two shape outliers, the total variation depth is used in the functional boxplot to detect magnitude outliers and we find seven with one shown in Figure 8(c). To have a better insight into the most representative sample of the sea surface height, we show the median in Figure 8(a). We see that compared to the median, which is treated as the most representative realization among the 50 ensemble runs, the shape outlier has a different pattern in the northern part of the Red Sea. From the median as shown in Figure 8(a), there is a connected area that has extremely low height in the northern part of the Red Sea, but in the shape outlier as shown in Figure 8(b), the area of extremely low height tends to be isolated. For the detected magnitude outlier as shown in Figure 8(c), the highest and lowest heights have a larger deviation from zero, as indicated by the dark red in the center part of the Red Sea and dark blue in the northern part of the Red Sea.