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1. Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models

   24 Oct 2024 | Contributor(s):: Mostafa Cham, Tartela Tabassum, Ehsan Shakeri, Jianwu Wang

   Accurate estimation of subglacial bed topography is crucial for understanding ice sheet dynamics and their responses to climate change. In this study, we employ machine learning models, enhanced with Spark parallelization, to predict subglacial bed elevation using surface attributes such as ice...

2. Greenland paleo sea level indicators and proxies derived from the GAPSLIP database

   02 Oct 2024 | Contributor(s):: Evan James Gowan

   This is a spreadsheet that contains the paleo sea level indicators and proxies that were compiled and described by Gowan (2023) in the GAPSLIP paleo sea level database for Greenland. Included are the datasets in ODS and tab delimited formats. The dataset includes 1019 data points, with 647 marine...

3. MEaSUREs Greenland Ice Mapping Project (GIMP) Land Ice and Ocean Classification Masks as Shape Files

   11 Sep 2024 | Contributor(s):: Ivan Parmuzin, Beata Maria Csatho

   The MEaSUREs Greenland Ice Mapping Project (GIMP) Land Ice and Ocean Classification Mask, Version 1 is now available in GIS friendly vector format (shape files) on Ghub! Shape files were converted from 30 m resolution raster masks and provide in WGS 84/UTM N24 (EPSG: 32624) and ...