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.
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