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

Sensors

Yu-Hsin Cheng²  Chung-Ru Ho¹  Zhe-Wen Zheng²  Yung-Hsiang Lee² 

[1] Department of Marine Environmental Informatics, National Taiwan Ocean University / 2 Pei-Ning Road, Keelung 20224, Taiwan;

关键词: multi-sensors;    integration filtering algorithm;    cold dome;    Kuroshio;   

DOI  :  10.3390/s90705521

来源: mdpi

【 摘 要 】

Multi-sensor data from different satellites are used to identify an upwelling area in the sea off northeast Taiwan. Sea surface temperature (SST) data derived from infrared and microwave, as well as sea surface height anomaly (SSHA) data derived from satellite altimeters are used for this study. An integration filtering algorithm based on SST data is developed for detecting the cold patch induced by the upwelling. The center of the cold patch is identified by the maximum negative deviation relative to the spatial mean of a SST image within the study area and its climatological mean of each pixel. The boundary of the cold patch is found by the largest SST gradient. The along track SSHA data derived from satellite altimeters are then used to verify the detected cold patch. Applying the detecting algorithm, spatial and temporal characteristics and variations of the cold patch are revealed. The cold patch has an average area of 1.92 × 10⁴ km². Its occurrence frequencies are high from June to October and reach a peak in July. The mean SST of the cold patch is 23.8 °C. In addition to the annual and the intraseasonal fluctuation with main peak centered at 60 days, the cold patch also has a variation period of about 4.7 years in the interannual timescale. This implies that the Kuroshio variations and long-term and large scale processes playing roles in modifying the cold patch occurrence frequency.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland