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CMAD: Advancing Understanding of Anomalous Melt Events over the Antarctic Sea Ice
28 Oct 2024 | Contributor(s):: Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja
Antarctic Sea ice extent reached a new record low of 1.965 million km2 on 23rd February 2023 (~ 32% below climatological values). We need to understand how the melting occurred during the August 2022 to February 2023. Did anomalous melting occur over there? Where did...
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Deep Learning for Antarctic Sea Ice Anomaly Detection and Prediction: A Two-Module Framework
28 Oct 2024 | Contributor(s):: Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja
The Antarctic sea ice cover plays a crucial role in regulating global climate and sea level rise. The recent retreat of the Antarctic Sea Ice Extent and the accelerated melting of ice sheets (which causes sea level rise) raise concerns about the impact of climate change. Understanding the spatial...
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CMAD: Advancing Understanding of Geospatial Clusters of Anomalous Melt Events in Sea Ice Extent
28 Oct 2024 | Contributor(s):: Maloy Kumar Devnath, Sudip Chakraborty, Vandana Janeja
Traditional statistical analyses do not reveal the spatial locations and the temporal occurrences of clusters of anomalous events that are responsible for a significant loss of sea ice extent. To address this problem, we present a novel method named Convolution Matrix Anomaly Detection (CMAD)....
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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, Josephine Namayanja (contributor)
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, Josephine Namayanja (contributor)
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, Josephine Namayanja (contributor)
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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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...