Spatial characterization of the meltwater field from icebergs in the Weddell Sea.
Identifieur interne : 000052 ( PubMed/Corpus ); précédent : 000051; suivant : 000053Spatial characterization of the meltwater field from icebergs in the Weddell Sea.
Auteurs : John J. Helly ; Ronald S. Kaufmann ; Maria Vernet ; Gordon R. StephensonSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 1091-6490 ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- geographic : Antarctic Regions.
- methods : Data Collection, Informatics, Information Dissemination, Remote Sensing Technology.
- trends : Informatics.
- Ecosystem, Freezing, Geographic Information Systems, Geography, Ice Cover, Oceans and Seas, Salinity, Temperature.
Abstract
We describe the results from a spatial cyberinfrastructure developed to characterize the meltwater field around individual icebergs and integrate the results with regional- and global-scale data. During the course of the cyberinfrastructure development, it became clear that we were also building an integrated sampling planning capability across multidisciplinary teams that provided greater agility in allocating expedition resources resulting in new scientific insights. The cyberinfrastructure-enabled method is a complement to the conventional methods of hydrographic sampling in which the ship provides a static platform on a station-by-station basis. We adapted a sea-floor mapping method to more rapidly characterize the sea surface geophysically and biologically. By jointly analyzing the multisource, continuously sampled biological, chemical, and physical parameters, using Global Positioning System time as the data fusion key, this surface-mapping method enables us to examine the relationship between the meltwater field of the iceberg to the larger-scale marine ecosystem of the Southern Ocean. Through geospatial data fusion, we are able to combine very fine-scale maps of dynamic processes with more synoptic but lower-resolution data from satellite systems. Our results illustrate the importance of spatial cyberinfrastructure in the overall scientific enterprise and identify key interfaces and sources of error that require improved controls for the development of future Earth observing systems as we move into an era of peta- and exascale, data-intensive computing.
DOI: 10.1073/pnas.0909306108
PubMed: 21444769
Links to Exploration step
pubmed:21444769Le document en format XML
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<author><name sortKey="Vernet, Maria" sort="Vernet, Maria" uniqKey="Vernet M" first="Maria" last="Vernet">Maria Vernet</name>
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<front><div type="abstract" xml:lang="en">We describe the results from a spatial cyberinfrastructure developed to characterize the meltwater field around individual icebergs and integrate the results with regional- and global-scale data. During the course of the cyberinfrastructure development, it became clear that we were also building an integrated sampling planning capability across multidisciplinary teams that provided greater agility in allocating expedition resources resulting in new scientific insights. The cyberinfrastructure-enabled method is a complement to the conventional methods of hydrographic sampling in which the ship provides a static platform on a station-by-station basis. We adapted a sea-floor mapping method to more rapidly characterize the sea surface geophysically and biologically. By jointly analyzing the multisource, continuously sampled biological, chemical, and physical parameters, using Global Positioning System time as the data fusion key, this surface-mapping method enables us to examine the relationship between the meltwater field of the iceberg to the larger-scale marine ecosystem of the Southern Ocean. Through geospatial data fusion, we are able to combine very fine-scale maps of dynamic processes with more synoptic but lower-resolution data from satellite systems. Our results illustrate the importance of spatial cyberinfrastructure in the overall scientific enterprise and identify key interfaces and sources of error that require improved controls for the development of future Earth observing systems as we move into an era of peta- and exascale, data-intensive computing.</div>
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