Observations of internal waves with high sampling data of radar altimetry and MODIS images

Zhang, Xudong; Zhang, Jie; Fan, Chenqing; Wang, Jing; Meng, Junmin

doi:10.1080/01431161.2018.1470700

Cited by 13 publications

(1 citation statement)

References 23 publications

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“…A higher peak value of the buoyancy frequency indicates stronger ocean stratification and a more considerable density difference between the upper and lower layers of the ocean. The propagation speed of IWs is proportional to the density difference [41]. This result explains why IW propagated faster in the rainy season in Case 3-1, while the peak values in Case 4-2 were close in the dry and rainy seasons.…”

Section: Influence Of Seasonal Variationsmentioning

confidence: 78%

A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea

Zhang

Zheng

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Internal waves (IWs) are broadly distributed globally and have significant impacts on offshore engineering and underwater navigation. The prediction of IW propagation is a challenging task because of the complex factors involved. In this study, a machine-learning model was developed to predict IW propagation in the Andaman Sea. The model is based on a back-propagation neural network trained by 1189 IW samples, including the crest length and the peak-to-peak distance of IWs, extracted from 123 Moderate-Resolution Imaging Spectroradiometer (MODIS) images and 33 Ocean Land Color Instrument (OLCI) images acquired from 2015 to 2019 and corresponding ocean environment parameters. Using the leading wave crest within an IW packet as input, we ran the model to forecast the IW locations and compare them with satellite observations. The average root-mean-square difference between the model-forecasted and satellite-observed IW leading crest after one tidal cycle was 3.21 km. The corresponding averaged correlation coefficient was 0.95 and the average Fréchet Distance was 11.46 km. We reiterated the model run over two tidal periods and obtained similar statistical results, indicating the robustness of forecasting IW packets. We find that reducing the time step helped to improve forecasting accuracy. The influence of input errors and seasonal variations on model results are discussed and an analysis shows that the initial propagation direction introduced to the model is necessary for cross-propagating IW patterns. Comparisons with the Korteweg-de Vries equation results show that the developed model has better performance and is more robust.

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Section: Influence Of Seasonal Variationsmentioning

confidence: 78%