An Algorithm for Cold Patch Detection in the Sea off Northeast Taiwan Using Multi-Sensor Data

Area of Science:

• Oceanography
• Remote Sensing
• Marine Ecology

Background:

• Upwelling significantly impacts marine ecosystems and fisheries.
• Identifying and characterizing upwelling zones is crucial for understanding ocean dynamics.

Purpose of the Study:

• To develop and apply a novel algorithm for detecting and analyzing upwelling-induced cold patches using multi-sensor satellite data.
• To investigate the spatial, temporal, and variability characteristics of the cold patch off northeast Taiwan.

Main Methods:

• Utilized sea surface temperature (SST) data (infrared and microwave) and sea surface height anomaly (SSHA) data from satellite altimeters.
• Developed an integration filtering algorithm based on SST to detect cold patches, identifying centers by maximum negative deviation and boundaries by SST gradient.
• Verified cold patch detection using along-track SSHA data.

Main Results:

• The upwelling cold patch has an average area of 1.92 × 10(4) km(2) and a mean SST of 23.8 °C.
• High occurrence frequencies were observed from June to October, peaking in July.
• The cold patch exhibits annual, intraseasonal (60-day peak), and interannual (4.7-year period) variations.

Conclusions:

• The developed algorithm effectively identifies and characterizes upwelling cold patches using multi-sensor satellite data.
• The study highlights the significant influence of Kuroshio current variations and large-scale processes on the cold patch dynamics.
• Findings provide valuable insights into the oceanographic conditions influencing Taiwan's marine environment.