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Evaluating Machine Learning and Statistical Models for Greenland Bed Topography
15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bed topography in Greenland using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet...
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Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography
15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...
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REU_Final_Presentation
15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, Jianwu Wang
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...
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Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography
15 May 2024 | Contributor(s):: Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan LI, Mathieu Morlighem, Jianwu Wang
Abstract:The purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for...
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Briner presentation on "Seeking Interglacial Isotopes"
27 Apr 2023 | Contributor(s):: Jason Briner
nothing
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CESM ISMIP6 Forcing Data
20 Oct 2021 | | Contributor(s):: Kate Thayer-Calder, Gunter Leguy, William Lipscomb
The Community Earth System Model (CESM) version 2.1 is a world-class coupled climate system model that includes components for the atmosphere, ocean, terrestrial system, river run-off, and fully active glaciers (Danabasoglu et al. 2020). This version of CESM was used in many experiments as part...
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ISMIP6 21st Century Greenland Projections
01 Oct 2021 | | Contributor(s):: sophie nowicki, Erika Simon, ISMIP6 team
This dataset provides the Greenland ice sheet model output produced as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6, Eyring et al., 2016; Nowicki et al. 2016). These simulations focus on 21st century evolution of the Greenland ice sheet under selected...
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ISMIP6 initMIP-Greenland simulations
01 Oct 2021 | | Contributor(s):: sophie nowicki, Erika Simon, ISMIP6 team
This dataset contains the initMIP-Greenland model simulations from the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). As described in Nowicki et al. (2016) and Goelzer et al. (2019), the initMIP-Greenland experiments focus on ice sheet initialization for the Greenland ice sheet...
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ISMIP6 21st Century Forcing Datasets
01 Oct 2021 | | Contributor(s):: sophie nowicki, Erika Simon, ISMIP6 Team (contributor)
These datasets contain the 21st century atmospheric and oceanic forcing datasets used for Greenland and Antarctic standalone ice sheet model simulations as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). ISMIP6 is a targeted activity of the Climate and...
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ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for the Cryosphere model Comparison Tool (CmCt) Input for Greenland and Antarctica
29 Sep 2021 | | Contributor(s):: Erika Simon, Sophie Nowicki, Jack Saba, Tom Newman, Matthew Beckley, Denis Felikson, Ritu Basnet
These datasets contain the ICESat, ERS1, ERS2, Envisat Laser and Radar Altimetry Datasets for CmCt Input data for Greenland and Antarctica. These reference observational datasets are used in the CmCt to compare ice sheet models with.The ICESat/GLAS instrument was a lidar altimeter and the...
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IceBridge ATM L2 Icessn Elevation, Slope, and Roughness
23 Sep 2021 | | Contributor(s):: Ash Narkevic, Ivan Parmuzin, Beata Maria Csatho, Greg Babonis
This data set contains IceBridge ATM L2 Icessn Elevation, Slope, and Roughness data (Studinger, M. 2014, updated 2020) organized into individual flight lines, both in ascii and ArcGIS shape file formats. The data were collected as part of NASA's Operation...
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Mass Balance and Velocity Data for Greenland Ice Sheet at High Elevations
17 Sep 2021 | | Contributor(s):: Beata Maria Csatho, Ash Narkevic, Ivan Parmuzin
This is an estimation of mass balance of the Greenland Ice Sheet at higher elevations, computed as the difference between the estimated annual total snow accumulation and ice discharge. Measurements are taken at 161 stations located 30 km apart, at 2000 m elevation that circumnavigates Greenland....
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Greenland Surface Strain Rates & Stresses
15 Sep 2021 | | Contributor(s):: Kristin Poinar
These datasets are 2D principal strain rates and principal stresses across the surface of the Greenland Ice Sheet.We started with representative surface velocities over a 20-year period (Joughin et al., 2016). We smoothed the velocities with a 1 km × 1 km boxcar filter, which carries...
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Greenland Ice Surface Temperature, Surface Albedo, and Water Vapor from MODIS Comparison Tool
26 Jan 2021 | | Contributor(s):: Denis Felikson, Erika Simon, Dorothy K. Hall, Nicolo DiGirolamo, Elliot Snitzer
Compare observations of Greenland Ice Surface Temperature, Surface Albedo, and Water Vapor from MODIS against MERRA-2 reanalysis model output.
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ATM-Based Crevasse Detection & Extraction workflow
29 Jul 2020 | | Contributor(s):: Renette Jones-Ivey, Jeanette Sperhac, Kristin Poinar
ABCDE Tool