By Topic

A Multi-Tier Provenance Model for Global Climate Research

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Stephan, E. ; Pacific Northwest Nat. Lab., Richland, WA, USA ; Halter, T. ; Gibson, T. ; Beagley, N.
more authors

Global climate researchers rely upon many forms of sensor data and analytical methods to help profile subtle changes in climate conditions. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program provides researchers with curated Value Added Products (VAPs) resulting from continuous instrumentation streams, data fusion, and analytical profiling. The ARM operational staff and software development teams (data producers) rely upon a number of techniques to ensure strict quality control (QC) and quality assurance (QA) standards are maintained. Climate researchers (data consumers) are highly interested in obtaining as much provenance evidence as possible to establish data trustworthiness. Currently all the evidence is not easily attainable or identifiable without significant efforts to extract and piece together information from configuration files, log files, codes, or status information on the ARM website. Our objective is to identify a provenance model that serves the needs of both the VAP producers and consumers. This paper shares our initial results - a comprehensive multi-tier provenance model. We describe how both ARM operations staff and the climate research community can greatly benefit from this approach to more effectively assess and quantify the data historical record.

Published in:

Network-Based Information Systems, 2009. NBIS '09. International Conference on

Date of Conference:

19-21 Aug. 2009