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Updated: Jun 26, 2026

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton
Published on: July 28, 2023
Data-thinning algorithms for "over-sampled" multi-parameter ocean optics data.
Jeffrey H Smart1, Kevin T Barrett
1The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd, Laurel, MD 20723-6099, USA. Smartjh1@jhuapl.edu
This study presents a new method to reduce over-sampled oceanographic data from gliders and paravanes. The adaptive approach preserves scientific information while decreasing data density for efficient analysis.
Area of Science:
- Oceanography
- Data Science
- Marine Technology
Background:
- High-resolution oceanographic datasets are increasingly available from autonomous platforms like gliders and towed paravanes.
- These valuable datasets are often over-sampled in space and time, leading to storage and processing challenges.
Purpose of the Study:
- To develop and demonstrate a data-adaptive method for reducing the spatio-temporal density of oceanographic profiles.
- To ensure that crucial scientific information is retained despite data reduction.
Main Methods:
- A user-configurable algorithm was developed for data sub-sampling.
- The method retains data at fixed intervals and selectively adds samples based on significant changes in profile depth extent or values.
Main Results:
- The described method effectively reduces data density while preserving essential scientific content.
- An example application on 5,000 chlorophyll fluorescence profiles from Australian waters demonstrates the algorithm's efficacy.
Conclusions:
- The data-adaptive method offers an efficient solution for managing large oceanographic datasets.
- This approach facilitates the analysis of high-resolution oceanographic data from autonomous systems.
