Abstract
While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running computeintensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.
Original language | English |
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Title of host publication | Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2018 |
Publisher | Association for Computing Machinery, Inc |
Pages | 42-46 |
Number of pages | 5 |
ISBN (Electronic) | 9781450360418 |
DOIs | |
State | Published - Nov 6 2018 |
Event | 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2018 - Seattle, United States Duration: Nov 6 2018 → Nov 6 2018 |
Publication series
Name | Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2018 |
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Conference
Conference | 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2018 |
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Country/Territory | United States |
City | Seattle |
Period | 11/6/18 → 11/6/18 |
Funding
This manuscript has been co-authored by employees of UT-Battelle, under contract DE-AC05-00OR22725 with the US Department of Energy. The authors would also like to acknowledge the financial and intellectual support for this research by the Integrated Assessment Research Program of the US Department of Energy's Office of Science, Biological and Environmental Research (DOE BER). This work is supported in part by NSF ACI-1541215. Œis manuscript has been co-authored by employees of UT-BaŠelle, under contract DE-AC05-00OR22725 with the US Department of Energy. Œe authors would also like to acknowledge the financial and intellectual support for this research by the Integrated Assessment Research Program of the US Department of Energy’s Oce of Science, Biological and Environmental Research (DOE BER). Œis work is supported in part by NSF ACI-1541215.
Keywords
- Analysis
- Climate Data
- Geospatial Analysis
- Visualization