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Stochastic models for GRACE/GRACE-FO accelerometers and inter-satellite ranging instruments

Cite as:

Murböck, Michael; Flechtner, Frank; Abrykosov, Petro; Pail, Roland (2023): Stochastic models for GRACE/GRACE-FO accelerometers and inter-satellite ranging instruments. GFZ Data Services. https://doi.org/10.5880/nerograv.2023.001

Status

I   N       R   E   V   I   E   W : Murböck, Michael; Flechtner, Frank; Abrykosov, Petro; Pail, Roland (2023): Stochastic models for GRACE/GRACE-FO accelerometers and inter-satellite ranging instruments. GFZ Data Services. https://doi.org/10.5880/nerograv.2023.001

Abstract

This data publication represents the main outcomes of WP4.100 of Individual Project IP4 and of the Deliverable D4.1 of the research unit NEROGRAV summarizing the analyses of the GRACE and GRACE-FO accelerometer (ACC) and satellite-to-satellite tracking data (Microwave instrument (MWI) or Laser Ranging Interferometer (LRI)) in order to derive a characterization of the instrument performance and a stochastic model. A detailed description and discussion focusing on the GRACE data is given in Murböck et al. (submitted to Remote Sensing).

This first version of the combined ACC+MWI/LRI noise models is provided with the ASCII-file NEROGRAV_Dataset_GRACE_GRACE-FO_ACC-MWI-LRI_StochasticModel_V01.dat containing header information (17 lines) and the square root power spectral densities (PSDs), i.e. the amplitude spectral densities (ASDs) for the combined accelerometer and ranging observations in terms of range-rates (cf. Fig. 1). It is given for 21600 frequencies from 1/86400 Hz up to 0.25 Hz. Above 0.1 Hz (Nyquist frequency of the 5 s sampled MWI data) the columns for the ACC+MWI models are zero. The five columns consist of the frequency in Hz (col. 1), the combined ACC+MWI models for GRACE 2007 (col. 2), GRACE 2014 (col. 3), GRACE-FO 2019 (col. 4) and the combined GRACE-FO 2019 ACC+LRI model (col. 5) in m/s/√Hz.

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

  • Murböck, Michael;Technical University Berlin, Berlin, Germany;GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Flechtner, Frank;Technical University Berlin, Berlin, Germany;GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Abrykosov, Petro;Technical University München. Munich, Germany
  • Pail, Roland;Technical University München. Munich, Germany

Contact

  • Flechtner, Frank; GFZ German Research Centre for Geosciences, Potsdam, Germany;

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    License: CC BY 4.0

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