Quality Controlled Ocean Temperature Archive (QuOTA), 1778-2005

Progress Code: completed Maintenance and Update Frequency: notPlanned Statement: Data source: original field data Data processing and quality control by Ann Gronell in the Oceans and Climate subprogram, Ocean Observing Systems (Rick Bailey). For full details, refer to documentation QuOTA site. Credi...

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Bibliographic Details
Other Authors: CSIRO O&A, Information & Data Centre (pointOfContact), CSIRO Oceans & Atmosphere - Hobart (hasAssociationWith), Cowley, Rebecca (pointOfContact)
Format: Dataset
Language:unknown
Published: Australian Ocean Data Network
Subjects:
CTD
Online Access:https://researchdata.edu.au/quality-controlled-ocean-1778-2005/686933
Description
Summary:Progress Code: completed Maintenance and Update Frequency: notPlanned Statement: Data source: original field data Data processing and quality control by Ann Gronell in the Oceans and Climate subprogram, Ocean Observing Systems (Rick Bailey). For full details, refer to documentation QuOTA site. Credit NOAA-IPRC, CMAR IOTA (Indian Ocean Thermal Archive) Credit Ken Ridgway Credit Rebecca Cowley Credit Susan Wijffels Credit Paul Barker Credit Ann Gronell Credit Jeff Dunn. Many people were involved in collection of data over many years Credit multiple platforms were used including over 100 vessels. The QuOTA project involved NOAA-IPRC and CMAR jointly undertaking to build a very high quality ocean thermal data archive by applying methods and expertise developed through the NOAA-IPRC/CMAR IOTA (Indian Ocean Thermal Archive) collaboration which was established in 1998. The Quota Project resulted in building a high quality upper ocean temperature dataset for the Indian Ocean and the South-western Pacific (east of the dateline). QuOTA contains ocean temperature data collected since 1778 and includes XBT, CT, CU, CTD, XCDT, MBT, BT, BA, DT, SST, TE, UO, bottle, drifting and moored bouy data. Quality control of the data is done by automated processes, followed by 'hand-QC' of data that fails the automated test. This results in a data set containing very little 'bad' data and any that remains is usually subtly faulty, having little impact on most analyses.