Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on p...
Main Authors: | , , , , , |
---|---|
Format: | Text |
Language: | unknown |
Published: |
Purdue University
2023
|
Subjects: | |
Online Access: | https://docs.lib.purdue.edu/iguide/2023/presentations/3 https://docs.lib.purdue.edu/context/iguide/article/1022/viewcontent/Reducing_Uncertainty_in_Sea_level_Rise_Prediction__A_Spatial_variability_aware_Approach.pdf |
id |
ftpurdueuniv:oai:docs.lib.purdue.edu:iguide-1022 |
---|---|
record_format |
openpolar |
spelling |
ftpurdueuniv:oai:docs.lib.purdue.edu:iguide-1022 2023-12-17T10:21:39+01:00 Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach Ghosh, Subhankar An, Shuai Sharma, Arun Gupta, Jayant Shekhar, Shashi Subramanian, Aneesh 2023-10-05T17:30:00Z application/pdf https://docs.lib.purdue.edu/iguide/2023/presentations/3 https://docs.lib.purdue.edu/context/iguide/article/1022/viewcontent/Reducing_Uncertainty_in_Sea_level_Rise_Prediction__A_Spatial_variability_aware_Approach.pdf unknown Purdue University https://docs.lib.purdue.edu/iguide/2023/presentations/3 https://docs.lib.purdue.edu/context/iguide/article/1022/viewcontent/Reducing_Uncertainty_in_Sea_level_Rise_Prediction__A_Spatial_variability_aware_Approach.pdf I-GUIDE Forum Applied Statistics Artificial Intelligence and Robotics Atmospheric Sciences Climate Databases and Information Systems Data Science Dynamical Systems Geographic Information Sciences Geological Engineering Geology Geophysics and Seismology Glaciology Human Geography Hydrology Numerical Analysis and Scientific Computing Ocean Engineering Oceanography Operations Research Systems Engineering and Industrial Engineering Other Computer Sciences Other Earth Sciences Other Environmental Sciences Other Geography Other Mathematics Other Physical Sciences and Mathematics Physical and Environmental Geography Remote Sensing Spatial Science Urban Studies and Planning Water Resource Management text 2023 ftpurdueuniv 2023-11-23T18:29:31Z Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale. Text Antarc* Antarctic Greenland Ice Ice Shelf permafrost Purdue University: e-Pubs Antarctic Greenland |
institution |
Open Polar |
collection |
Purdue University: e-Pubs |
op_collection_id |
ftpurdueuniv |
language |
unknown |
topic |
Applied Statistics Artificial Intelligence and Robotics Atmospheric Sciences Climate Databases and Information Systems Data Science Dynamical Systems Geographic Information Sciences Geological Engineering Geology Geophysics and Seismology Glaciology Human Geography Hydrology Numerical Analysis and Scientific Computing Ocean Engineering Oceanography Operations Research Systems Engineering and Industrial Engineering Other Computer Sciences Other Earth Sciences Other Environmental Sciences Other Geography Other Mathematics Other Physical Sciences and Mathematics Physical and Environmental Geography Remote Sensing Spatial Science Urban Studies and Planning Water Resource Management |
spellingShingle |
Applied Statistics Artificial Intelligence and Robotics Atmospheric Sciences Climate Databases and Information Systems Data Science Dynamical Systems Geographic Information Sciences Geological Engineering Geology Geophysics and Seismology Glaciology Human Geography Hydrology Numerical Analysis and Scientific Computing Ocean Engineering Oceanography Operations Research Systems Engineering and Industrial Engineering Other Computer Sciences Other Earth Sciences Other Environmental Sciences Other Geography Other Mathematics Other Physical Sciences and Mathematics Physical and Environmental Geography Remote Sensing Spatial Science Urban Studies and Planning Water Resource Management Ghosh, Subhankar An, Shuai Sharma, Arun Gupta, Jayant Shekhar, Shashi Subramanian, Aneesh Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
topic_facet |
Applied Statistics Artificial Intelligence and Robotics Atmospheric Sciences Climate Databases and Information Systems Data Science Dynamical Systems Geographic Information Sciences Geological Engineering Geology Geophysics and Seismology Glaciology Human Geography Hydrology Numerical Analysis and Scientific Computing Ocean Engineering Oceanography Operations Research Systems Engineering and Industrial Engineering Other Computer Sciences Other Earth Sciences Other Environmental Sciences Other Geography Other Mathematics Other Physical Sciences and Mathematics Physical and Environmental Geography Remote Sensing Spatial Science Urban Studies and Planning Water Resource Management |
description |
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale. |
format |
Text |
author |
Ghosh, Subhankar An, Shuai Sharma, Arun Gupta, Jayant Shekhar, Shashi Subramanian, Aneesh |
author_facet |
Ghosh, Subhankar An, Shuai Sharma, Arun Gupta, Jayant Shekhar, Shashi Subramanian, Aneesh |
author_sort |
Ghosh, Subhankar |
title |
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
title_short |
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
title_full |
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
title_fullStr |
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
title_full_unstemmed |
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach |
title_sort |
reducing uncertainty in sea-level rise prediction: a spatial-variability-aware approach |
publisher |
Purdue University |
publishDate |
2023 |
url |
https://docs.lib.purdue.edu/iguide/2023/presentations/3 https://docs.lib.purdue.edu/context/iguide/article/1022/viewcontent/Reducing_Uncertainty_in_Sea_level_Rise_Prediction__A_Spatial_variability_aware_Approach.pdf |
geographic |
Antarctic Greenland |
geographic_facet |
Antarctic Greenland |
genre |
Antarc* Antarctic Greenland Ice Ice Shelf permafrost |
genre_facet |
Antarc* Antarctic Greenland Ice Ice Shelf permafrost |
op_source |
I-GUIDE Forum |
op_relation |
https://docs.lib.purdue.edu/iguide/2023/presentations/3 https://docs.lib.purdue.edu/context/iguide/article/1022/viewcontent/Reducing_Uncertainty_in_Sea_level_Rise_Prediction__A_Spatial_variability_aware_Approach.pdf |
_version_ |
1785537118497406976 |