Ocean Eddy Identification and Tracking using Neural Networks

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.Comment: accepted for International Geoscience and Remote Sensing Symposium 201

Leistungen der ökologischen Landwirtschaft zur Vermeidung von Stoffeinträgen in das Grund- und Oberflächenwasser im Vergleich zu konventioneller Bewirtschaftung

Stoffeinträge aus der Landwirtschaft in das Grund- und Oberflächenwasser stellen ein hohes Umweltrisiko dar. In einer systematischen Literaturübersicht wurden ökologische und konventionelle Landwirtschaft hinsichtlich der jeweiligen Belastungen des Grund- und Oberflächenwassers im Vergleich untersucht. Insgesamt wurden 96 Vergleichsstudien mit 308 Vergleichpaaren evaluiert. 63% der Vergleichpaare zeigen Vorteile des ökologischen Landbaus in Bezug auf Nitratausträge, genauso 90% der Vergleichspaare in Bezug auf Pestizidbelastungen. Im Schnitt (Median) können unter ökologischer Bewirtsvchaftung 28% weniger Nitratauswaschungen festgestellt werden als in der konventionellen Landwirtschaft, zudem werden keine Pestizide und vermutlich auch weniger Tierarzneimittel in das Grund- und Oberflächenwasser ausgewaschen. Hinsichtlich des Austrags von Phosphor ist die Datenlage unklar. Das System der ökologischen Landwirtschaft zeigt ein hohes Potential im Schutz von Grund- und Oberflächenwasser

The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model

We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5{\deg}, covering the time frame 2003 to 2019 without gaps, and including uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the Ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the global average over more than 1000 stations, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands. While some differences exist, overall GLWS2.0 agrees quite well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.Comment: Preprin

Developing a Complex Independent Component Analysis (CICA) technique to extract non-stationary patterns from geophysical time series

In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of real-valued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an ICA algorithm based on diagonalization of fourth-order cumulants is then applied to decompose the new complex dataset in (a), and finally, (c) the dominant independent complex modes are extracted and used to represent the dominant space and time amplitudes and associated phase propagation patterns. The performance of CICA is examined by analyzing synthetic data constructed from multiple physically meaningful modes in a simulation framework, with known truth. Next, global terrestrial water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) gravimetry mission (2003–2016), and satellite radiometric sea surface temperature (SST) data (1982–2016) over the Atlantic and Pacific Oceans are used with the aim of demonstrating signal separations of the North Atlantic Oscillation (NAO) from the Atlantic Multi-decadal Oscillation (AMO), and the El Niño Southern Oscillation (ENSO) from the Pacific Decadal Oscillation (PDO). CICA results indicate that ENSO-related patterns can be extracted from the Gravity Recovery And Climate Experiment Terrestrial Water Storage (GRACE TWS) with an accuracy of 0.5–1 cm in terms of equivalent water height (EWH). The magnitude of errors in extracting NAO or AMO from SST data using the complex EOF (CEOF) approach reaches up to ~50% of the signal itself, while it is reduced to ~16% when applying CICA. Larger errors with magnitudes of ~100% and ~30% of the signal itself are found while separating ENSO from PDO using CEOF and CICA, respectively. We thus conclude that the CICA is more effective than CEOF in separating non-stationary patterns

Calibration of the Latest Generation Superconducting Gravimeter iGrav-043 Using the Observatory Superconducting Gravimeter OSG-CT040 and the Comparisons of Their Characteristics at the Walferdange Underground Laboratory for Geodynamics, Luxembourg

In December 2019, the latest generation transportable superconducting gravimeter (SG) iGrav-043 purchased by the University of Bonn was installed in the Walferdange Underground Laboratory for Geodynamics (WULG) in the Grand Duchy of Luxembourg. In this paper, we estimate the calibration factor of the iGrav-043, which is essential for long-term gravity monitoring. We used simultaneously collected gravity data from the un-calibrated iGrav-043 and the calibrated Observatory superconducting gravimeter OSG-CT040 that operates continuously at WULG since 2002. The tidal analysis provides a simple way to transfer the calibration factor of one SG to the other. We then assess and compare tidal analyses, instrumental drifts and high frequency noises. After 20 years of continuous operation, the instrumental drift of the OSG-CT040 is almost zero. From 533 days of joint operation, we found that the instrumental drift of iGrav-043 exhibits a composite behavior: just after the setup and for two months a fast exponential decrease of 171 nm s−2, then a linear with a rate of 66 nm s−2 ± 10 nm s−2 per year. We suggest that a period of 3 months is sufficient for calibrating the iGrav. Accidental electrical power cuts triggered slight differences in the reaction and recovery of the OSG-CT040 and iGrav-043. However, it has been found that the long-term linear behavior of the drift was not affected