Data-thinning algorithms for "over-sampled" multi-parameter ocean optics data.
The deployment of towed depth-profiling paravane systems and autonomous gliders is providing a wealth of high-resolution oceanographic datasets. These datasets are, however, over-sampled in space and time. This paper describes a data-adaptive, user-configurable method that has been used to significantly reduce the time/space density of such data without compromising the inherent scientific information that they provide. The method involves sub-sampling at fixed space and time intervals with additional samples being kept given either a significant change (1) in the depth extent of the alongtrack profiles, or (2) in the values of the profiles themselves. An example is provided showing how well the algorithm works on nearly 5,000 chlorophyll fluorescence profiles collected off the coast of Australia.
Smart
Jeffrey H
JH
The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd, Laurel, MD 20723-6099, USA. Smartjh1@jhuapl.edu
Barrett
Kevin T
KT
D000465:Algorithms; D001315:Australia; D002734:Chlorophyll / Q000032:analysis*; D003196:Computer Graphics; D003198:Computer Simulation; D005453:Fluorescence; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D055095:Optics and Photonics; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software
D002734:Chlorophyll