Mid-Term Simultaneous Spatiotemporal Prediction of Sea Surface Height Anomaly and Sea Surface Temperature Using Satellite Data in the South China Sea

Shao, Qi; Li, Wei; Hou, Guangchao; Han, Guijun; Wu, Xiaobo

doi:10.1109/lgrs.2020.3042179

Cited by 11 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in Shao et al. (2020), the validity of ocean dynamic model is generally no more than 15 days, this is the issue MCM model wants to address.…”

Section: Methodsmentioning

confidence: 99%

“…It should be noted that as the length of the input time series increases, so does the number of IMFs that EMD can obtain. In the prediction tasks that shown in Figure 2, the CEEMD method needs to decompose 30-day PCs, like Shao et al (2020). In this study, PCs with a duration of 9,496 days from 1 January 1993, to 31 December 2018 will be decomposed and then input the MLP model for training.…”

Section: Complete Ensemble Empirical Mode Decomposition (Ceemd)mentioning

confidence: 99%

“…S2S forecasting bridges the gap between the more-mature weather and seasonal climate prediction communities, which is important (Vitart & Robertson, 2018). As in Shao et al (2020), the validity of ocean dynamic model is generally no more than 15 days, this is the issue MCM model wants to address.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

“…For example, the South China Sea (SCS), as one of the largest marginal seas in the world, is characterized by its multiscale physical processes (Miao et al, 2021). Numerous studies that use ML method to forecast the oceanic components in the SCS, including sea surface temperature anomaly (SSTA) (Shao et al, 2020;Xiao et al, 2019), sea surface height (SSH) or sea level anomaly (SLA) (Liu et al, 2020;Shao et al, 2020;Song et al, 2021), as well as mesoscale eddy trajectory prediction (X. Wang et al, 2020).…”

mentioning

confidence: 99%

See 3 more Smart Citations

The Rossby Normal Mode as a Physical Linkage in a Machine Learning Forecast Model for the SST and SSH of South China Sea Deep Basin

Lin,

Zhang,

Zhang

et al. 2023

JGR Oceans

View full text Add to dashboard Cite

Machine learning (ML) has been widely applied in Earth sciences, but its black‐box nature still remains. Existing techniques for interpretable ML developed by deep learning researchers are not satisfactory in understanding physical problems. In this study, our objective is to establish the physical linkage between features extracted from ML model and results derived from physical equations that describe the problem. This approach aims to provide a more comprehensible way of understanding an ML model. We select a less complex ML forecasting model, consisting of traditional statistical algorithms, which effectively forecast sea surface temperature anomaly (SSTA) and sea level anomaly (SLA) of the South China Sea (SCS) in 30 days ahead. Here, we focus on the SLA prediction and detect the physical mechanism of model capability to predict SCS SLA. Previous study has identified Rossby normal modes as the physical mechanism for long‐lived eddies in SCS in previous study. We here demonstrate the relationship between the ML model and Rossby normal modes in SCS in terms of temporal variations and spatial distributions of water mass. The model's skill in predicting Rossby normal modes partially explains its skill in predicting SLA, thereby providing a more transparent and interpretable basis for forecasts in SCS.

show abstract

“…As in Shao et al. (2020), the validity of ocean dynamic model is generally no more than 15 days, this is the issue MCM model wants to address.…”

Section: Methodsmentioning

confidence: 99%

Section: Complete Ensemble Empirical Mode Decomposition (Ceemd)mentioning

confidence: 99%

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

The Rossby Normal Mode as a Physical Linkage in a Machine Learning Forecast Model for the SST and SSH of South China Sea Deep Basin

Lin,

Zhang,

Zhang

et al. 2023

JGR Oceans

View full text Add to dashboard Cite

show abstract

“…In 2018, Zhang et al [36] used the EEMD-LSTM model to forecast land surface temperature and proved the hybrid model is superior to the signal neural network. Shao et al [37] combined the empirical orthogonal function of multivariate, CEEMD, and multilayer perceptron to forecast SST considering the correlation of different variables in the real marine environment. In 2019, Wu et al [38] used the CEEMD-BP model to forecast SSTA and proved that the prediction result of the CEEMD-BP outperforms EEMD-BP, but this conclusion is not absolute.…”

Section: >mentioning

confidence: 99%

Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

Liu

Guo

et al. 2022

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

View full text Add to dashboard Cite

The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multi-step-ahead forecasting for SSTA is greatly challenging since the nonlinearity and non-stationarity of SSTA and the lag problem of prediction. Therefore, in this paper, a multi-step-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions (IMFs) that are more stationary than the original data. Then GRU is used for multi-step forecasting, in which three multistep forecasting strategies (recursive, direct, and multi-output) are compared. The proposed hybrid model is validated by multi-step forecasting for monthly average SSTA at the Niño3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multi-month trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Niño3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.

show abstract

The CNN-GRU model with frequency analysis module for sea surface temperature prediction

2023

View full text Add to dashboard Cite

Sea surface temperature is an important parameter of ocean hydrology. Accurate prediction of sea surface temperature is of great significance for ocean economic development and extreme weather prevention. Application of deep learning based method in sea surface temperature prediction has significantly increased due to its high analytical power. Nevertheless, sea surface temperature time series are so volatile and stochastic, leading to the fact that in depth analysis and accurate prediction of sea sea surface temperature are still challenging. Considering non-stationary and nonlinear characteristics in sea surface temperature sequence, variational mode decomposition is adopted as the de-noising module to reduce the influence of noise. Furthermore, the convolutional neural network is combined with the gated recurrent unit network to extract both spatial and temporal features in sea surface temperature sequence, simultaneously. Finally, a sea surface temperature prediction model based on deep learning model with frequency analysis module is proposed in this paper. The sea surface temperature of East China Sea is selected for empirical study. Through comparisons with the benchmarked models and existing deep learning based models, it is proved that the proposed model holds a relatively stable, efficient and effective performance in sea surface temperature prediction.

show abstract

Mid-Term Simultaneous Spatiotemporal Prediction of Sea Surface Height Anomaly and Sea Surface Temperature Using Satellite Data in the South China Sea

Cited by 11 publications

References 20 publications

The Rossby Normal Mode as a Physical Linkage in a Machine Learning Forecast Model for the SST and SSH of South China Sea Deep Basin

The Rossby Normal Mode as a Physical Linkage in a Machine Learning Forecast Model for the SST and SSH of South China Sea Deep Basin

Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

The CNN-GRU model with frequency analysis module for sea surface temperature prediction

Contact Info

Product

Resources

About