Publication Date:
2018-10-01
Description:
Biases in expendable bathythermograph (XBT) instruments have emerged as a leading uncertainty in reconstructions of historical ocean heat content change and therefore climate change. Corrections for these biases depend on the type of XBT used; however, this is unspecified for 52% of the historical XBT profiles in the World Ocean Database. Here, we use profiles of known XBT type to train a neural network that can classify probe type based on three covariates: profile date, maximum recorded depth, and country of origin. Whereas previous studies have shown an average classification skill of 77%, falling below 50% for some periods, our new algorithm maintains an average skill of 90%, with a minimum of 70%. Our study illustrates the potential for successfully applying machine learning approaches in a wide variety of instrument classification problems in order to promote more homogeneous climate data records.
Print ISSN:
0739-0572
Electronic ISSN:
1520-0426
Topics:
Geography
,
Geosciences
,
Physics
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