Project Grant 2530967
- The National Science Foundation (NSF) awarded a $505,036 Project Grant under the Geosciences program (CFDA 47.050) to the Trustees of Dartmouth College to improve predictions of sea level rise driven by ice mass loss from the Greenland and Antarctic ice sheets. The project aims to combine physical understanding of ice dynamics with artificial intelligence techniques applied to satellite imagery datasets, in order to develop physics-informed models of basal sliding and ice calving processes. This...
- This $411,096 federal Project Grant award was provided by the National Science Foundation's Polar Programs (CFDA 47.078) to The Leland Stanford Junior University (Stanford University). The project aims to utilize machine learning and physics-based models to map and understand how surface meltwater on the Greenland ice sheet reaches the bedrock, which can impact ice sheet dynamics and response to climate change. Key products and services to be delivered include: Development of open-source deep...
- The National Science Foundation awarded a $269,223 Project Grant to the University of Texas at Austin from the Polar Programs federal grant program (CFDA 47.078) to support research titled "Collaborative Research: Machine-Enabled Modeling of Terminus Ablation for Greenland's Outlet Glaciers." The research aims to improve projections of sea-level rise from Greenland through developing physics-based modeling of ice-ocean interactions at outlet glaciers using machine learning analysis...
- This Project Grant award from the National Science Foundation's (NSF) Polar Programs (CFDA 47.078) is funding research to improve the accuracy of forecasts for future sea-level rise. The $782,557 award, active from August 1, 2025 to July 31, 2030, aims to develop efficient artificial intelligence (AI) representations of complex glacier processes to incorporate into ice-sheet models without substantially increasing computational costs. The research will condense three specific glacier processes...
- This $191,868 National Science Foundation project grant supports research to understand surface-to-bed meltwater pathways across the Greenland Ice Sheet. Funded through the Polar Programs grant program (CFDA 47.078), the award will utilize machine learning and physics-based models to map cracks and draining lakes on the ice sheet from satellite imagery. Researchers at the University of Kansas Center for Research will develop open-source artificial intelligence codes to automatically detect ice...
- This $127,350 project grant from the National Science Foundation Division of Polar Programs, under the Polar Programs federal grant program (CFDA 47.078), will support collaborative research to improve modeling of terminus ablation for Greenland's outlet glaciers. The researchers will use machine learning to analyze glaciological observations and better understand the variables influencing the ice-ocean boundary. They will develop an equation to represent ice-ocean interactions in an ice sheet...
- This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will support a collaborative research effort to develop novel artificial intelligence (AI) methods for automated sea ice classification. The University of Colorado will receive $317,922 to apply weakly supervised learning techniques that can effectively utilize existing, irregularly-labeled data to improve the performance of sea ice mapping and monitoring. The project aims to address key...
- This National Science Foundation (NSF) Integrative Activities (CFDA 47.083) project grant, valued at $286,102, will fund research at the University of North Dakota (UND) to develop advanced ice flow modeling capabilities using graphics processing units (GPUs). The goal is to better assess the Antarctic ice sheet's contribution to sea level rise and its sensitivity to climate change uncertainties. Specifically, the principal investigator and a graduate student will investigate an accelerated,...
- The University of Montana received a $364,234 project grant award from the National Science Foundation Division of Polar Programs on September 1, 2021 to conduct collaborative research titled "INTEGRATING DATA AND MODELING TO QUANTIFY RATES OF GREENLAND ICE SHEET CHANGE, HOLOCENE TO FUTURE." The research is being conducted under the NSF's Polar Programs, with a focus on strengthening fundamental knowledge and understanding of polar regions as outlined in the program's CFDA #47.078....
- This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $596,821 to the University of Washington to develop new mathematical models and simulation tools for studying the calving of icebergs from glaciers. The project aims to improve predictions of sea-level rise by advancing the understanding of glacier terminus evolution and iceberg calving, which is a critical but poorly modeled aspect of...
This $600,397 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund collaborative research to develop innovative, physics-informed machine learning models for predicting ice dynamics and calving processes that contribute to global sea level rise. The University of Montana will lead this 3-year project, which aims to improve projections of how rapidly the Greenland ice sheet will respond to climate change by combining physical understanding of ice dynamics with artificial intelligence techniques applied to satellite imagery data. The research will focus on the three glaciers with the largest ice discharge on the Greenland ice sheet, training neural networks on historical data from 1980-2010 and then testing the models on observations from 2010-2020. The project will contribute to reducing uncertainty in sea level rise projections, which are critical for coastal planning and policy decisions. It will also train the next generation of scientists through a glaciology and machine learning summer school and further develop open-source software tools for the broader scientific community.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.4k | 7/16/25 |