Search

Geodetic applications of altimetry have largely been inversions of gravity anomaly. Literatures wherein Earth's gravity gradient tensor has been studied mostly presented only the vertical gravity gradient. However, there are six unique signals that constitute the gravity gradient tensor. Gravity gradients are signals suitable for detecting short-wavelength topographic and tectonic features. They are derived from double differentiation of the geoid (or disturbing potential); and hence, are susceptible to noise amplification which was exacerbated by low across-track resolution of altimetry data in the past. However, current generation of altimetry observations have improved spatial resolutions, with some better than 5 km. Therefore, this study takes advantage of current high-resolution altimetry datasets to present CUGB2023GRAD, a global (latitudinal limits of ±80°) 1 arc-minute model of Earth's gravity gradient tensor over the oceans using deflection of the vertical as inputs in the wavenumber domain. The results are first assessed via Laplace's equation; whereby the resultant residual gradient is virtually zero everywhere except at high latitudes - icy environments known for contaminating altimetry observations. Due to the absence of similar models from other institutions, the results are further assessed by comparing the vertical tensor component, zzT, with equivalent models from Scripps Institution of Oceanography (SIO) and Technical University of Denmark (DTU). The DTU equivalents were derived by multiplying their gravity anomalies by 2 k ? in the wavenumber domain. Analysis showed that the inverted zzT averagely deviates from the DTU and SIO equivalents by 0.09 and 0.18 E with corresponding standard deviations of 3.55 and 6.96 E, respectively. Bathymetric coherence analysis of zzT 20 over a section of the western Pacific showed comparable results with the reference models. This study proves that current generation of altimetry geodetic missions can effectively resolve Earth's gravity gradient tensor. The CUGB2023GRAD model data can be freely accessed at https://doi.org/10.5281/zenodo.7710254 (Annan et al., 2023). [ABSTRACT FROM AUTHOR]

The French coast of the Eastern English Channel (ECC) is classified as potential eutrophication zone by the Paris and Oslo Convention (OSPAR), and as moderate to poor according to phytoplankton quality element of the Water Framework Directive (WFD). Indeed, the French part of the EEC is regularly affected by Phaeocystis globosa bloom events, which have detrimental effects on the marine ecosystem, economy as well as public health. Since phytoplankton is an important indicator of water quality, the MAREL Carnot oceanographic multi-sensor station was installed in the Eastern English Channel at the Carnot wall in Boulogne sur Mer in 2004. The aim of this station was to monitor water quality and phytoplankton in order to complement results from existing more conventional low resolution monitoring programs, with high frequency data (sampling every 20 minutes). The purpose of this paper is to introduce the MAREL Carnot dataset and show how it can be used for several research objectives. MAREL Carnot collects high frequency, multi-parameter observations from surface water, as well as meteorological measurements, and send data almost immediately to an inshore data center. In this paper, we present several physiochemical and biological parameters measured by this station. In addition, we demonstrated, based on previous research activities, that the MAREL Carnot dataset is useful for evaluating environmental or ecological statuses, marine phytoplankton ecology, physical oceanography, turbulence, as well as public policy. Most importantly, we showed its contribution to Marine Strategy Framework Directive (MSFD) and other regional or universal conventions. [ABSTRACT FROM AUTHOR]

This document describes the dataset of depth (pressure)/temperature profiles collected by means of the AdriFOOS infrastructure in the period 2012-2020 and briefly illustrates the structure of this facility, the oceanographic dataset validation procedures, some of the results achieved, and also lists some possible operational applications. In fact, in the last decade, CNR IRBIM's AdriFOOS has collected an enormous amount of georeferenced oceanographic and catch data through the use of commercial fishing vessels operating in the Adriatic Sea. This information is of the utmost importance to provide data to feed oceanographic models and advance knowledge on climate change, as well as to improve the ecosystem approach to fisheries management. [ABSTRACT FROM AUTHOR]

Over a hundred of experiments were realised between 1998 and 2022 in the Mediterranean Sea using surface Lagrangian drifters, at coastal and offshore level. Raw data were initially unified and pre-processed manually by eliminating spikes and wrong positions or date/time information. The integrity of the received data packages was checked, and incomplete ones were discarded. Deployment information was retrieved from an initial excel database and campaign notes for each drifter and integrated into the PostgreSQL database, realised and maintained by the National Institute of Oceanography and Applied Geophysics (OGS) in Trieste (IT). This database also collects a variety of metadata about the drifter model, project, owner and operator. Subsequently data were processed using standard procedures of editing and quality control developed for the OGS Mediterranean drifter dataset to remove spikes generated by malfunctioning of the sensors and obtain files with common characteristics. Drifter data and plots of each track were also visually checked to remove any point not identified by the automatic procedure and clearly erroneous. Drifters' trajectories were split into two or more segments that have been considered as different deployments, in case of specific drifters' behaviours. Data were interpolated at defined time intervals to temporarily unify tracks. From the original 138 experiments, a dataset of 204 tracks was obtained, available from the public open-access repository in SEANOE (SEA scieNtific Open data Edition) at https://doi.org/10.17882/90537 (Ribotti et al., 2022). [ABSTRACT FROM AUTHOR]

Measurements targeting mesoscale and submesoscale processes in the ice-covered part of the Arctic Ocean are sparse in all seasons. As a result, there are significant knowledge gaps with respect to these processes, in particular related to the role of eddies and fronts in the coupled ocean-atmosphere-sea ice system. Here we present a unique observational dataset of upper ocean temperature and salinity collected by a set of buoys installed on ice floes as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network. The multi-sensor systems, each of them equipped with five temperature and salinity recorders on a 100 m long inductive modem tether, drifted together with the main MOSAiC ice camp through the Arctic Transpolar Drift between October 2019 and August 2020. They transmitted hydrographic in situ data via the iridium satellite network at 10 minute intervals. While three buoys failed early due to ice dynamics, five of them recorded data continuously for 10 months. Four units were successfully recovered in early August 2020, additionally yielding internally stored instrument data at 2 minute intervals. The raw datasets (Hoppmann et al., 2021i) were merged, processed, quality-controlled and validated using independent measurements. Upon acceptance of the manuscript, the finally processed dataset (currently under moratorium) will be made publicly available under Hoppmann et al. (2022i). As an important part of the MOSAiC physical oceanography program, this unique dataset has many synergies with the manifold co-located observational datasets, and is expected to yield significant insights into ocean processes, and to contribute to the validation of high-resolution numerical simulations. [ABSTRACT FROM AUTHOR]