Time series of monthly combined HLSST and SLR gravity field models to bridge the gap between GRACE and GRACE-FO: QuantumFrontiers_HLSST_SLR_COMB2019s

DOI

QuantumFrontiers_HLSST_SLR_COMB2019s is a series of monthly gravity field models based on high-low satellite-to-satellite (HLSST) tracking and satellite laser ranging (SLR) data up to degree and order 60. The combination of HLSST and SLR data is done on the normal equation level using Variance Component Estimation. The series spans from 2003 to 2018 and thus covers the entire period between GRACE and GRACE Follow-On. It is therefore a prime candidate to bridge the data gap between these two satellite mission considering long-wavelength features on a global scale. The model has been developed with data contributions from the Astronomical Institute, University Bern (AIUB), the Institute of Geodesy, Theoretical Geodesy and Satellite Geodesy, Graz University of Technology, the Institute for Geodesy, Leibniz University Hannover and the European Space Agency. More details on the processing can be found in "Time-Variable Gravity Signal in Greenland Revealed by High-Low Satellite-to-Satellite Tracking" (Weigelt et al, 2013, https://doi.org/10.1002/jgrb.50283) Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2123 QuantumFrontiers – 390837967.

Identifier
DOI https://doi.org/10.5880/ICGEM.2019.008
Related Identifier https://doi.org/10.1029/2011JB008916
Related Identifier https://www.geoq.uni-hannover.de/383.html
Related Identifier https://doi.org/10.1002/jgrb.50283
Metadata Access http://doidb.wdc-terra.org/oaip/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:doidb.wdc-terra.org:6818
Provenance
Creator Weigelt, Matthias
Publisher GFZ Data Services
Contributor Ince, Elmas Sinem; Reißland, Sven
Publication Year 2019
Funding Reference Deutsche Forschungsgemeinschaft http://doi.org/10.13039/501100001659 Crossref Funder ID EXC-2123/1 CRC 1128: Relativistic Geodesy and Gravimetry with Quantum Sensors (geo-Q)
Rights CC BY 4.0; http://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Language English
Resource Type Dataset
Format application/octet-stream
Size 1 Files
Discipline Geodesy, Geoinformatics and Remote Sensing
Spatial Coverage (-180.000W, -90.000S, 180.000E, 90.000N)