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Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt using Deep Learning Technique
23 Jul 2024 | Contributor(s):: Sahara Ali, Omar Faruque, Yiyi Huang, Md Osman Gani, Aneesh Subramanian, Nicole Schlegel, Jianwu Wang
Over the recent decades, Earth scientists have noted a more pronounced shift in climate patterns near the polar regions, specifically the Arctic, in comparison to the rest of the Earth. The increased warming is largely attributed to the diminishing ice cover in the Arctic, which causes solar...
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Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
23 Jul 2024 | Contributor(s):: Sahara Ali, Omar Faruque, Yiyi Huang, Aneesh Subramanian, Nicole-Jienne Shchlegel, Md Osman Gani, Jianwu Wang
The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect...
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Benchmarking probabilistic machine learning models for arctic sea ice forecasting
23 Jul 2024 | Contributor(s):: Sahara Ali, Seraj Mostafa, Xingyan Li, Sara Khanjani, Jianwu Wang, James Foulds, Vandana Janeja
The Arctic is a region with unique climate features, motivating new AI methodologies to study it. Unfortunately, Arctic sea ice has seen a continuous decline since 1979. This not only poses a significant threat to Arctic wildlife and surrounding coastal communities but is also adversely affecting...
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MT-IceNet - A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
23 Jul 2024 | Contributor(s):: Sahara Ali, Jianwu Wang
Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades. The essential part of Arctic amplification is the unprecedented sea ice loss as demonstrated by satellite observations....
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Incorporating Causality with Deep Learning in Predicting Short-Term and Seasonal Sea Ice
16 Jul 2024 | Contributor(s):: Emam Hossain, Sahara Ali, Yiyi Huang, Nicole Schlegel, Jianwu Wang, Aneesh Subramanian, Md Osman Gani
Abstract: Arctic sea ice (ASI) is playing a pivotal role in keeping global warming under control. However, the recently amplified decreasing sea ice trend has become a major concern. Since satellites started monitoring the ASI in 1979, every decade the Arctic has lost 13.1% of sea ice and the...
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A Survey on Causal Discovery Methods for IID and Time Series Data
16 Jul 2024 | Contributor(s):: Uzma Hasan, Emam Hossain, Md Osman Gani
Abstract: The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data with certain assumptions. Over the...
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Visualizing Glacier/Ocean Change
21 May 2024 | Contributor(s):: Sophie Goliber, John Erich Christian
A simple 1-D glacier model to teach students about glacier/ocean changes.
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Evaluating Machine Learning and Statistical Models for Greenland Bed Topography
15 May 2024 | Contributor(s):: Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Jianwu Wang, Sikan Li, Mathieu Morlighem, Omar Faruque
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):: Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Sikan Li, Mathieu Morlighem, Omar Faruque, 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):: Homayra Alam, Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Omar Faruque, Sikan Li, Mathieu Morlighem
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):: Homayra Alam, Jianwu Wang, Tartela Tabassum, Katherine Yi, Angelina Dewar, Jason Lu, Ray Chen, Omar Faruque, Mathieu Morlighem, Sikan LI
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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Discovery of multi-domain spatiotemporal associations
26 Apr 2024 | Contributor(s):: Prathamesh Walkikar, Lei Shi, Bayu Adhi Tama, Vandana Janeja
This paper focuses on the discovery of unusual spatiotemporal associations across multiple phenomena from distinct application domains in a spatial neighborhood where each phenomenon is represented by anomalies from the domain. Such an approach can facilitate the discovery of interesting links...
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Development and Initial Testing of XR-Based Fence Diagrams for Polar Science
23 Apr 2024 | Contributor(s):: Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, Don Engel
Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, and Don Engel. 2023. Development and Initial Testing of XR-Based Fence Diagrams for Polar Science. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA. IEEE,...
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Metrics for the Quality and Consistency of Ice Layer Annotations
23 Apr 2024 | Contributor(s):: Naomi Tack, Bayu Adhi Tama, Atefah Jebeli, Vandana P. Janeja, Don Engel, Rebecca Williams
Naomi Tack, Bayu Adhi Tama, Atefeh Jebeli, Vandana P. Janeja, Don Engel, and Rebecca Williams. 2023. Metrics for the Quality and Consistency of Ice Layer Annotations. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA....
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Visualizing the Greenland Ice Sheet in VR using Immersive Fence Diagrams
23 Apr 2024 | Contributor(s):: Naomi Tack, Rebecca Williams, Nicholas Holschuh, Sharad Sharma, Don Engel
Naomi Tack, Rebecca Williams, Nicholas Holschuh, Sharad Sharma, and Don Engel. 2023. Visualizing the Greenland Ice Sheet in VR using Immersive Fence Diagrams. In Practice and Experience in Advanced Research Computing, ACM, Portland OR USA, 429–432. DOI:...
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Initial Development of a WebXR Platform for Ice Penetrating Radar Data, to Improve our Understanding of Polar Ice Sheets.
23 Apr 2024 | Contributor(s):: Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, Don Engel
Tack, Naomi, Holschuh, Nicholas, Sharma, Sharad, Williams, Rebecca, and Engel, Don. “Initial Development of a WebXR Platform for Ice Penetrating Radar Data, to Improve our Understanding of Polar Ice Sheets.” Poster at the AGU23 meeting, IN43B-0627, December 2023
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AskICE-D: A querying tool for the ICE-D project
15 Feb 2024 | Contributor(s):: Joseph P Tulenko, Greg Balco, jason briner, Sophie Goliber
A tool that helps users build and send SQL queries to the ICE-D database and dynamically returns the query results.
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THEPORE
06 Feb 2024 | Contributor(s):: Gilda Maria Currenti, Rosalba Napoli, Santina Chiara Stissi
THEPORE (THErmo-POro-Elastic solutions) is an open source software to perform forward and inverse modelling of the ground displacements induced by thermo-poro-elastic sources. The software, implemented in the MATLAB environment, offers a library of analytical and semi-analytical solutions to...
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Ice Sheet Simulation Compliance Checker
19 Jan 2024 | Contributor(s):: Renette Jones-Ivey, sophie nowicki, Sophie Goliber
Checks the compliance of a simulation dataset according criteria for ISMIP6
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proxy VIIRS polar observations from Aqua/Terra MODIS data (bands: I5, M12-16)
23 Nov 2023 | Contributor(s):: Dimitry Sushon
This example dataset from 11/14/2023 was produced from Aqua/Terra L1 MODIS observations over the Antarctic coast and over Siberia/Arctic Ocean.The input granules were processed with a VIIRS L1 Generator application to apply bow-tie effect and re-project the data from MODIS to VIIRS spatial...