Mesoscale eddies are widely found in the ocean. They play important roles in heat transport, momentum transport,
ocean circulation and so on. The automatic detection of mesoscale eddies based on satellite remote sensing images is an
important research topic. Some image processing methods have been applied to identify mesoscale eddies such as Canny
operator, Hough transform and so forth, but the accuracy of detection was not very ideal. This paper described a new
algorithm based on watershed segmentation algorithm for automatic detection of mesoscale eddies from sea level
anomaly(SLA) image. Watershed segmentation algorithm has the disadvantage of over-segmentation. It is important to
select appropriate markers. In this study, markers were selected from the reconstructed SLA image, which were used to
modify the gradient image. Then two parameters, radius and amplitude of eddy, were used to filter the segmentation
results. The method was tested on the Northwest Pacific using TOPEX/Poseidon altimeter data. The results are
encouraging, showing that this algorithm is applicable for mesoscale eddies and has a good accuracy. This algorithm has
a good response to weak edges and extracted eddies have complete and continuous boundaries. The eddy boundaries
generally coincide with closed contours of SSH.
The physical forcing and biological response are highly variable over multi-time scales ranging from biennial to interdecadal in the tropical Pacific Ocean. Satellite provides a systematic view of the coupled biological-physical variability over large spatial scales. Based on the satellite observation data from 1985 to 2011, we analyzed the multi-timescale variabilities of the physical ocean parameters (sea surface temperature, sea level anomaly, wind, rainfall) and the biological ocean parameter (chlorophyll-a) as well as the linkages between them over the tropical Pacific Ocean. The goal of this study was to investigate the multi-timescale variability and spatial-temporal association patterns of the physical-biological paramters in the tropical Pacific Ocean of the year from 1985 to 2011.
El Niño continues the most important coupled ocean-atmosphere phenomenon to cause global climate variability on
seasonal to inter annual time scales. The first independent spatial mode which carried out by EOF analysis of tropical
and north Pacific sea surface temperature (SST) for the period of 1985-2009 in AVHRR dataset is found to be associated
with well-known regional climate phenomena: the El Niño. This paper addresses the need for a reliable El Niño index
that allows for the historical definition of El Niño events in the instrumental record back to 1985-2009 with a new
perspective. For quantitative purposes, possible definitions are explored that match the El Niño identified historically in
1985-2009, and it is suggested that an El Niño can be said to occur if difference of sea surface temperature (SST)
anomalies between the tropical and north Pacific exceeds 0.6 times standard deviation for 5 months or more. An
advantage of such a definition is that it combines the characteristics between tropical and north Pacific. Through seasonal
analysis of SST in El Niño event, we found that the El Niño events are almost beginning in boreal spring or perhaps
boreal summer and peak from November to February. It provides a more complete and flexible description of the El
Niño phenomenon than single area in tropical Pacific.
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