| dc.description.abstract | Ocean is a very important source of energy for tropical cyclones, the amount of energy provided by the ocean will determine the trend of its intensity. In order to be able to predict the intensity of tropical cyclones, the temperature structure of the upper ocean is needed to determine the amount of ocean heat content. With the current technology, we cannot quickly obtain the ocean thermal structure, so increasing the method of obtaining the ocean thermal structure is an important topic today.
In this study, we have established three regression models: (1) Use Argo Floats from 2000 to 2008 (19,915 Argo Floats) to build the regression model, called Pun2014, (2) Use Argo Floats from 2000 to 2017 (87,580 Argo Floats) to build the regression model, called Y18_FW, (3) Use the dynamic window method to improve the spatial resolution of Y18_FW, called Y18_DY. Then use 6502 independent Argo Floats in 2018 to verify the three regression models.
In the verification of the Northwest Pacific, the three regression models performed very well in estimating the temperature profile, and their results were also very similar. In the verification of different latitudes, we found that the performance of the model will be different due to the number and distribution of Argo Floats in each latitude. The low latitude areas that originally had fewer Argo Floats will be significantly improved due to the increase in data volume, and the higher latitude areas will be affected by the spatial resolution. When the latitude is higher, the number of Argo Floats increases. Y18_FW with lower spatial resolution increases the root mean square error by 5-10%, and Y18_DY can effectively reduce this error.
We also established Y18_DY in the South China Sea and discussed the applicability of the model in the South China Sea. The research results show that the estimated thermal structure has a high correlation with Argo Floats, and the correlation coefficients of ocean heat content and T100 (average sea temperature of the upper 100 meters) are also higher than 0.8. The long-term temperature profile comparison with specific Argo Floats shows that Y18_DY can estimate the underwater temperature well, and the temperature change trend observed by Argo Floats can be effectively estimated. It shows that with the regression model, we can use SSHA to effectively estimate the ocean thermal structure. | en_US |