Sea surface temperature is an important physical parameter in marine research. Accurate prediction of sea surface temperature is important for coping with climate change, marine ecological protection, and marine economic development. In this study, the SST prediction performance of ConvLSTM and ST-ConvLSTM with different input lengths, prediction lengths, and hidden sizes is investigated. The experimental results show that: (1) The input length has an impact on the prediction results of SST, but it does not mean that the longer the input length, the better the prediction performance. ConvLSTM and ST-ConvLSTM have the best prediction performance when the input length is set to 1, and the prediction performance gradually decreases as the input length increases. (2) Prediction length affects SST prediction. As the prediction length increases, the prediction performance gradually decreases. When other parameters are kept constant and only the prediction length is changed, the ConvLSTM gets the best result when the prediction length is set to 2, and the ST-ConvLSTM gets the best result when the prediction length is set to 1. (3) The setting of the hidden size has a great influence on the prediction ability of the sea surface temperature, but the hidden size cannot be set blindly. For ST-ConvLSTM, although the prediction performance of SST is better when the hidden size is set to 128 than when it is set to 64, the consequent computational cost increases by about 50%, and the performance only improves by about 10%.

The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems.

Based on mooring array data from the South China Sea (SCS) Internal Wave Experiment, in this paper, five burst events of near‐inertial waves (NIWs) in the northern SCS that occurred during August 2010 to April 2011 are reported. Among these NIWs, three were induced by typhoons Lionrock, Meranti, and Megi in 2010. Due to the differences in typhoon characteristics, mooring measuring ranges, distances between typhoon centers and moorings, and local conditions, the NIWs induced by the three typhoons show various intensities and structures at all three moorings. An interesting phenomenon is that the weaker typhoon Meranti generates stronger NIWs than the stronger typhoon Megi, which is largely due to the larger translation speed of Meranti. The e‐folding time of near‐inertial variance suggests that the damping of typhoon‐induced NIWs is related to local conditions. In addition to the typhoon‐induced NIWs, two NIW burst events were found in December 2010 and March 2011 at one mooring; however, these events were absent from the two adjacent moorings. Through analysis, we ruled out the local winds, lateral propagation, and parametric subharmonic instability as the causes of these NIWs. Although the real mechanisms remain unknown, the NIWs that occurred in December 2010 and March 2011 exhibit different features from the typhoon‐induced NIWs. First, these NIWs have larger vertical wave numbers and smaller wavelengths than the typhoon‐induced NIWs. Second, these NIWs can cause intense shear in deep water, whereas intense shear of typhoon‐induced NIWs mainly exist in the upper ocean (approximately 150–200 m).

The effect of swell waves on atmospheric boundary layer turbulence under low winds was explored using data from a fixed platform located in the South China Sea. The wind spectra, cospectra, and Ogive curve measured at a height of 8 m above the mean sea surface provided direct evidence that wind stress was affected by swell waves. To interpret such phenomena, an improved approach was derived based on the fact that the total wind stress was the vector sum of turbulent stress and wave‐coherent stress. Different from the approaches of earlier studies, our approach did not align the turbulent stress with the mean wind speed. The influence of swell waves on the magnitude and direction of the total wind stress was analyzed using our approach. The results showed that the wave‐coherent stress derived from our data accounted for 32% of the total wind stress. The magnitude and angle of the wind stress changed by swell waves depended on the relative angle between the turbulent stress and swell direction.