Extended Abstract (16K)7.5
Data quality assessment: Why we need to share
Nathaniel B. Guttman, NOAA/NESDIS/NCDC, Asheville, NC
Climate data are used to solve practical problems in all societal endeavors. To reduce uncertainties in the observation and prediction of climate, we must institute a program of long-term information management. It is not until, and unless, all the observations are combined and analyzed in the context of one another that the complete picture of climate variability can be viewed. Information synthesis ensures the integrity and preservation of the most accurate climate observation record. Also, enhancement of the basic information technology infrastructure allows data sets to be provided to the widest possible array of users in the most cost effective and useful manner.
Data quality assessment is dependent on the kind of information inherent in the observation rather than on the network generating the data. Similar kinds of data (e.g., daily data observed at coop, first order, etc. sites) should be treated together with the same rules and algorithms in an integrated manner. Not only should data be integrated, but assessment techniques and procedures should also be integrated. Algorithms developed by the many entities assessing data should be linked into one unified system so that all basic climate data that are distributed to the public by various agencies are treated in the same manner.
The benefits of an integrated approach to data quality assessment include: 1) Reduction in quality assessment development and maintenance costs (fewer systems will be built); 2) Consistency of data quality assessment; 3) Consistency of data provided to users; 4) Integrating data reduces chances for errors and inconsistencies among data sets that span multiple observing networks and platforms; 5) Standardized products can more easily be developed for servicing software, data summarization, visualization, climate monitoring, etc.; and 6) The collective experience, expertise and wisdom of various data processing entities lead to a better product than is possible from individual efforts.
Session 7, Data Reliability, Quality Assessment and Usability (Room 619/620)
Thursday, 15 January 2004, 1:30 PM-5:30 PM, Room 619/620
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