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This paper describes a method consisting of both automated statistical screening and manual quality control through expert visual inspection, which produces a historical ocean temperature archive of high quality—that is, nearly all profiles are unique (duplicate elimination) and 95% of bad data is eliminated. The complete process involves comprehensive duplicate elimination, an unreasonable gradient check, and statistical screening to distill out suspect profiles, which are then only eliminated (or partially so) during an expert manual visual inspection step. Statistical screening was optimized using an archive of known quality. Two iterations of statistical screening were required to identify the bulk of the bad data. Of an archive of about 121 000 profiles, the authors found they had to manually inspect 35% of profiles to remove 95% of the bad data. While costly, they argue such an effort is worthwhile so that the historical ocean temperature archives, which have cost the global community millions of dollars to obtain, are made more immediately useful for climate and ocean sciences. An archive of upper ocean temperature profiles from the Indian Ocean is near completion and extensions into the Pacific Ocean have begun.

A Semiautomated Approach for Quality Controlling Large Historical Ocean Temperature Archives