ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0, 2002 - 2020
This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS)...
Main Authors: | , |
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Format: | Dataset |
Language: | English |
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NERC EDS Centre for Environmental Data Analysis
2021
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Online Access: | https://dx.doi.org/10.5285/36dae49c76f845a18062fa96599be719 https://catalogue.ceda.ac.uk/uuid/36dae49c76f845a18062fa96599be719 |
Summary: | This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). The Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021) The GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km² covering the entire AIS. A GMB basin product is also available as a separate dataset. Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736 |
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