A probabilistic approach to ship voyage reconstruction in ICOADS: A probabilistic approach to ship voyage reconstruction in ICOADS

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) provides the main archive for surface marine observations for the past approximately 150 years. ICOADS ship identifier (ID) information is often missing or unusable, preventing the linking of reports to an individual ship. A method for the reconstruction of ship voyages in ICOADS is presented, by which groups of reports can be associated with an individual ship or ship track. The method defines a function representing the probability density function (pdf ) of any particular report being associated with a group of reports. The parameters of the pdf are calculated from the ship data themselves, giving the likely variation of a ship report perpendicular to its overall direction of travel. For groups of reports with ID information, the PDF is used to associate reports without ID information with the known-ID track. Reports without ID information are then clustered together to form the most probable track. Results are shown for the period 1855–1969. Both the percentage of reports associated with tracks and the length of those tracks increase substantially following tracking. Initial validation of the results was performed by visual inspection: the model implementation was then refined to improve the results. Confidence in the tracking is increased by a demonstration that the method clusters together reports with similar sea surface temperature characteristics. Issues in the data were found to be one of the main challenges in implementing the tracking technique. Particular problems encountered included the coarse resolution of some position information; reports that were mispositioned in either space or time; unidentified duplicate reports; and the fragmentation of voyages between different ICOADS acquisition sources. Some of these effects could be ameliorated by pre-processing of ICOADS reports, however a full reprocessing of the historical input sources to ICOADS would be required to make further improvements.