Ocean eddies, known as the weather of the ocean, represent gyrating water masses that have horizontal scales from 10 km up to at times 500 km. They transport water mass, heat, nutrition, and carbon and have been identified as hot spots of biological activity. In radar altimetry, they affect alongtrack measurements of sea level height and lead to problems in the subsequent generation of sea level maps. Monitoring eddies is therefore of interest among others to marine biologists, oceanographers, and geodesists. In this paper, using occlusion sensitivity maps (OSMs) we investigate different neural network architectures that address the task of automatic detection of ocean eddies, which is challenging due to their spatio-temporal dynamic behavior. Thus we analyze the importance of the spatial context that is needed to infer correct semantics and compare them between the different architectures. For this, we use data from satellite altimetry since it offers sea surface heights precise enough to expose the presence of eddies. For detection, we utilize a transformer neural network called Teddy which can exploit temporal and spatial information in the data. For evaluating our approach, we use gridded data sets for the area of the western part of the southern Atlantic from 2000 to 2011. Our results are evaluated primarily by employing the dice score metric and show that transformers can infer the semantics with similar performance compared to state-of-the-art CNNs but at the same time are less sensitive towards structural changes due to different modeling of the spatial information of the data.

The Global Positioning System (GPS) is routinely used to measure the elastic response of Earth to continental hydrological mass changes, occurring at various temporal and spatial scales. While long-term and seasonal height changes are well observed in GPS height time series, the subseasonal deformations are less well resolved. A suite of predictions based on a remote sensing based Gravity Recovery and Climate Experiment (GRACE)-assimilating land surface model suggest significant high-frequency changes observable in GPS height time series (with periods between 15 and 90 days). This physical model is then adopted for separating the deterministic component of time series from the stochastic noise and is compared with the conventional harmonic functions modeling, a commonly used approach with predefined annual and semiannual periods. The noise parameters (spectral index and amplitude) associated with the two methods are estimated using the maximum likelihood estimation. We conclude that the GRACE-assimilated model output removes the effect of high-frequency hydrological deformations, producing less correlated residuals. Among other aspects, our results highlight the importance of assimilating GRACE-based remotely sensed total water storage data into hydrological models to obtain unbiased estimates of GPS vertical velocity and its uncertainty which is in demand in a range of applications such as the upcoming reference frame realizations.

Global climate change plays an essential role in our daily life. Mesoscale ocean eddies have a significant impact on global warming, since they affect the ocean dynamics, the energy as well as the mass transports of ocean circulation. From satellite altimetry we can derive high-resolution, global maps containing ocean signals with dominating coherent eddy structures. The aim of this study is the development and evaluation of a deep-learning based approach for the analysis of eddies. In detail, we develop an eddy identification and tracking framework with two different approaches that are mainly based on feature learning with convolutional neural networks. Furthermore, state-of-the-art image processing tools and object tracking methods are used to support the eddy tracking. In contrast to previous methods, our framework is able to learn a representation of the data in which eddies can be detected and tracked in more objective and robust way. We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.

This paper suggests an innovative analysis method for derivation of sea surface heights in coastal areas using conventional radar altimetric waveforms. Our analysis consists of a sub-waveform detection and leading edge identification, while using information from spatially and temporally neighboring waveforms. Sub-waveform detection is done via a sparse representation approach and spatial and temporal information is incorporated by utilizing a conditional random field. Our analysis method is combined with a weighted 3-parameter ocean model retracker. Experiments are conducted using Jason-2 Sensor Geophysical Data Records (SGDR) obtained over the Northern Bay of Bengal in region off the coast of Bangladesh.

Estimating accurate water heights from complex radar return waveforms, as observed by satellite altimeters over inland water bodies, requires the application of postprocessing algorithms known as retrackers. This study introduces a new retracking algorithm, the Institute for Theoretical Geodesy Retracker (ITGR), which is applied to TOPEX/Poseidon (T/P), Jason-1, and Jason-2 waveforms over African lakes. The ITGR algorithm first analyzes the pattern of returned waveforms to identify retrackable waveforms. To provide range corrections, it then applies a maximum-likelihood estimator to a flexible waveform model, which is constructed based on the number of identified peaks and their position in the waveform. ITGR also adopts an adjustable peak model to deal with both symmetric and asymmetric shaped peaks. As a result, the introduced method exhibits some skill in mitigating the impact of peaks in the waveform pattern. We validated our retracked lake level heights (LLHs) over Lake Volta by comparing them to daily tide gauge and LLHs derived from various retracking algorithms, as well as to LLHs from the Global Reservoir and Lake Monitoring database. Our results over Lake Volta indicate that ITGR LLHs can be obtained with a standard deviation of 33 cm from T/P, 10 cm from Jason-1, and 8 cm from the Jason-2 altimeter; this represents an improved performance when compared to existing retrackers. Investigations over a land-contaminated region of Lake Victoria confirm the superior performance of subwaveform retrackers such as ITGR. Over Lake Naivasha, where mostly specular peak waveform patterns were observed, the performance of these retrackers was found similar to that of conventional threshold retrackers.