Report of the International Workshop on Quality Control of Monthly Climate Data
Monthly climate data must rely on quality control techniques that are predominantly statistical. While the actual quality control may use numerical formulae or visual inspections of graphs, at the heart of most techniques are some basic statistical relationships. These relationships primarily fall into three categories: (1) relationships of data elements to themselves (e.g., outliers from long-term means), (2) relationships to nearby data (e.g., neighbor checks), and (3) relationships to some other data parameter (e.g., sea level pressure to station pressure). The purpose of this workshop was to discuss these data relationships and other quality control techniques, to relate experiencesin applying quality control to data, and to organize cooperation in the production of quality control software.
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