Taiwan is blessed to be surrounded by the rich natural resources of the ocean. However, our nation’s marine development is still the lack of understanding the ocean processes around Taiwan. Previously, Taiwan’s marine research has been focused on in situ observations. In situ observations of the ocean are costly, the coverage is limited to narrow areas, and it is virtually impossible to fully comprehend the ocean process and dynamics. Therefore, alternative dataset other than in situ observations is in need to understand seas around Taiwan. Satellite remote sensing data offer extensive and frequent measurements with reasonable spatial and temporal resolution over the vast area, although satellite measurements are limit to the sea surface. To retrieve the upper-ocean thermal structure, sea surface height anomaly (SSHA) data from satellite altimetry is used in this study because it has been well proved that variations in the depth of isotherm are closely related to variations in the SSHA field. Usually upper-ocean thermal structure is estimated by using satellite SSHA as inputs to ocean models or empirical methods. Currently, the NOAA/AOML employs a two-layer reduced gravity model together with satellite SSHA measurements to operational estimate global upper-ocean thermal structure. However, the model parameter used in the operation is tuned only for the Gulf of Mexico and the Atlantic Ocean. There is no validation to assess the accuracy in the rest of the global oceans. In fact, several studies demonstrated that the quality of this derived upper-ocean thermal structure does not work well in the northwestern Pacific Ocean. In this study, we will use satellite SSHA and sea surface temperature (SST) data as input to a two-layer reduced gravity model, which has been proved to be the most realistic method currently, to estimate upper-ocean thermal structure in the northwestern Pacific Ocean. After estimation, we will validate the estimated results with observational measurements from the NOAA/Global Temperature and Salinity Profile Program (GTSPP) and ARGO buoy data sets. With accurate prediction of the upper-ocean thermal structure by this study, numerous effects upon the weather, national defense, economics, fishery, disaster, and environmental problems can be ameliorated.
|Effective start/end date||2018/01/01 → 2018/12/31|
- Mixed layer depth
- Satellite altimeter
- linear regression model
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