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Sharing Satellite Observations with the Climate-Modeling Community: Software and Architecture.

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Title: Sharing Satellite Observations with the Climate-Modeling Community: Software and Architecture.
Authors: Crichton, Daniel J.1, Mattmann, Chris A.1, Cinquini, Luca1, Braverman, Amy1, Waliser, Duane1, Gunson, Michael1, Hart, Andrew F.1, Goodale, Cameron E.1, Lean, Peter2, Kim, Jinwon3
Source: IEEE Software. Sep2012, Vol. 29 Issue 5, p73-81. 9p. 1 Color Photograph, 4 Diagrams.
Subjects: Computer simulation of climate change, Climate change mathematical models, Artificial satellites, Remote sensing, Software architecture, Software engineering, Model-integrated computing
Abstract: The disparate communities of climate modeling and remote sensing are finding economic, political, and societal benefit from the direct comparisons of climate model outputs to satellite observations, using these comparisons to help tune models and to provide ground truth in understanding the Earth's climate processes. In the context of the Intergovernmental Panel on Climate Change (IPCC) and its upcoming 5th Assessment Report (AR5), the authors have been working with principals in both communities to build a software infrastructure that enables these comparisons. This infrastructure must overcome several software engineering challenges, including bridging heterogeneous data file formats and metadata formats, transforming swath-based remotely sensed data into globally gridded datasets, and navigating and aggregating information from the largely distributed ecosystem of organizations that house these climate model outputs and satellite data. The authors' focus in this article is on the description of software tools and services that meet these stringent challenges, and on informing the broader communities of climate modelers, remote sensing experts, and software engineers on the lessons learned from their experience so that future systems can benefit and improve upon their existing results. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Software is the property of IEEE Computer Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Sharing Satellite Observations with the Climate-Modeling Community: Software and Architecture.
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Software%22">IEEE Software</searchLink>. Sep2012, Vol. 29 Issue 5, p73-81. 9p. 1 Color Photograph, 4 Diagrams.
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  Data: The disparate communities of climate modeling and remote sensing are finding economic, political, and societal benefit from the direct comparisons of climate model outputs to satellite observations, using these comparisons to help tune models and to provide ground truth in understanding the Earth's climate processes. In the context of the Intergovernmental Panel on Climate Change (IPCC) and its upcoming 5th Assessment Report (AR5), the authors have been working with principals in both communities to build a software infrastructure that enables these comparisons. This infrastructure must overcome several software engineering challenges, including bridging heterogeneous data file formats and metadata formats, transforming swath-based remotely sensed data into globally gridded datasets, and navigating and aggregating information from the largely distributed ecosystem of organizations that house these climate model outputs and satellite data. The authors' focus in this article is on the description of software tools and services that meet these stringent challenges, and on informing the broader communities of climate modelers, remote sensing experts, and software engineers on the lessons learned from their experience so that future systems can benefit and improve upon their existing results. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Software is the property of IEEE Computer Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/MS.2012.21
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      – SubjectFull: Computer simulation of climate change
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      – SubjectFull: Climate change mathematical models
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      – SubjectFull: Artificial satellites
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      – SubjectFull: Model-integrated computing
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