Regularized Empirical Variance-Covariance-Matrices for stochastic gravity modeling of 8 major ocean tides

Cite as:

Sulzbach, Roman; Hart-Davis, Michael; Dettmering, Denise; Thomas, Maik (2023): Regularized Empirical Variance-Covariance-Matrices for stochastic gravity modeling of 8 major ocean tides. GFZ Data Services. https://doi.org/10.5880/nerograv.2023.003

Status

I N R E V I E W : Sulzbach, Roman; Hart-Davis, Michael; Dettmering, Denise; Thomas, Maik (2023): Regularized Empirical Variance-Covariance-Matrices for stochastic gravity modeling of 8 major ocean tides. GFZ Data Services. https://doi.org/10.5880/nerograv.2023.003

Abstract

This data publication represents the main outcomes of WP1.200 of Individual Project IP1 and Deliverable D1.1 of the research unit NEROGRAV. The goal of WP1.200 was the realistic representation of modern ocean tide model uncertainties in the form of empirical Variance-Covariance Matrices (VCMs) for the utilization in satellite gravimetric dealiasing. In the following, we describe the data set generation and format. A more detailed description of the processing strategy of the data set can be found in Abrykosov et al. (2021).

Additional Information

A deep understanding of mass distribution and mass transport in System Earth is needed to answer central questions in hydrology, oceanography, glaciology, geophysics and climate research. The necessary information is primarily derived from satellite mission data as observed by GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-on) describing the gravity field of the Earth and its temporal variations.

The research group (RG) „New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV)”, funded by the German Research Foundation (DFG), develops since May 2019 new analysis methods and modeling approaches to improve GRACE and GRACE-FO mission data analysis and focuses on geophysical applications that benefit from significantly reduced error levels in the time series of monthly gravity fields. Phase 1 lasted from May 2019 till April 2022. After successful evaluation in January 2022 the second phase started in January 2023.

The central hypothesis of the research group, slightly updated for phase 2, is: Only by concurrently improving and better understanding of sensor data, background models, and processing strategies of satellite gravimetry, the resolution, accuracy, and long-term consistency of mass transport series can be significantly increased; the science return in various fields of application improved and the potential of future technological sensor developments fully exploited.

All groups participating in NEROGRAV have a long-term heritage of expertise in geodetic data acquisition and modeling and will additionally contribute their unique complementary expertise from various neighboring disciplines such as oceanography, hydrology, solid Earth, geophysics and atmospheric and climate sciences. Therefore, it is expected that the second funding phase will not only create significantly improved GRACE/GRACE-FO gravity field models over two decades, but also enable geophysical applications based on this long-term series such as quantifying North Atlantic deep water transports as indicator for variations in the Atlantic Meridional Overturning Circulation (AMOC), assessment of hydrometeorological extreme events or identification of climatic signatures in variations of the terrestrial water storage. Important results and datasets of phase 1 can be found at GFZ Data Services.

Authors

Sulzbach, Roman;GFZ German Research Centre for Geosciences, Potsdam, Germany;Freie Universtität Berlin, Berlin, Germany
Hart-Davis, Michael;Deutsches Geodätisches Forschungsinstitut (DGFI-TUM), Munich, Germany
Dettmering, Denise;Deutsches Geodätisches Forschungsinstitut (DGFI-TUM), Munich, Germany
Thomas, Maik;GFZ German Research Centre for Geosciences, Potsdam, Germany;Freie Universtität Berlin, Berlin, Germany

Contact

Sulzbach (Roman) ; GFZ Potsdam, Fu Berlin; ➦

Contributors

Sulzbach

Keywords

satellite gravimetry, Stokes coefficients, Covariance, empirical VCM, NEROGRAV, New Refined Observations of Climate Change from Spaceborne Gravity Missions