Principle of Machine Learning and Its Potential Application in Cli-mate Prediction

He, Shengping; Wang, Huijun; Li, Hua; Zhao, Jiazhen

doi:10.32629/jai.v4i1.483

Cited by 6 publications

(5 citation statements)

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“…In recent years, machine learning methods have been used in climate prediction [16][17][18], and many successes in the prediction of sea surface temperature indexes beyond one year (>12 months) have been reported [19,20]. These successful applications of machine learning methods for predicting a climate phenomenon support this research work.…”

Section: Introductionsupporting

confidence: 52%

Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

et al. 2022

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The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift in the summer rain belt in eastern China. In this study, we developed a forecast system for the June WPSHRL index based on the latest autumn and winter sea surface temperature (SST). Considering the adverse condition created by the small observed sample size, a very simple neural network (NN) model was selected to extract the non-linear relationship between input predictors (SST) and target predictands (WPSHRL) in the forecast system. In addition, some techniques were used to deal with the small sample size, enhance the stabilization of the forecast skills, and analyze the interpretability of the forecast system. The forecast experiments showed that the linear correlation coefficient between the predictions from the forecast system and their corresponding observations was around 0.6, and about three-fifths of the observed abnormal years (the years with an obviously high or low WPSHRL index) were successfully predicted. Furthermore, sensitivity experiments showed that the forecast system is relatively stable in terms of forecast skill. The above results suggest that the forecast system would be valuable in real-life applications.

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Section: Introductionsupporting

confidence: 52%

Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

et al. 2022

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“…The CNN deep learning method commonly used in climate prediction [26,27] was used to obtain an extended-range forecast of RPECEs. Specifically, we predicted the time coefficient of low-frequency oscillations.…”

Section: Extended-range Forecastmentioning

confidence: 99%

Extended-Range Forecast of Regional Persistent Extreme Cold Events Based on Deep Learning

Wu,

Wang,

Wei

et al. 2023

Atmosphere

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Regional persistent extreme cold events are meteorological disasters that cause serious harm to people’s lives and production; however, they are very difficult to predict. Low-temperature weather systems and their effects have a significant low-frequency oscillation period (10–20 d and 30–60 d). This paper uses deep learning to analyze the extended-range time scale and predict regional persistent extreme cold events. The dominant low-frequency oscillation components of cold events are obtained via wavelet transform and Butterworth filtering. The low-frequency oscillation component is decomposed via empirical orthogonal function decomposition to extract the main spatial mode and time coefficient. A convolutional neural network is used to establish the correlation between large-scale circulations and the time coefficient of the low-frequency oscillation component of the lowest temperature. The proposed deep learning model exhibits good prediction accuracy for regional persistent extreme cold events with low-frequency oscillations.

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“…In recent years, the rise of machine learning has provided new possibilities for numerical forecasting. Machine learning has been applied in climate prediction [2], and many scholars have used machine learning to conduct research and find applications in the field of storm surges. Lei [3] used the recurrent neural network (RNN) to forecast storm surges and found that it can obtain better prediction results than the backpropagation (BP) neural network.…”

Section: Introductionmentioning

confidence: 99%

Storm surge forecasting based on physics-informed neural networks in the Bohai Sea

Fu,

Xiong,

2024

J. Phys.: Conf. Ser.

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Physics-informed neural networks (PINN), as a new method of integrating artificial neural networks (ANN) and physical laws, have been considered and applied in the fields of ocean forecasting and ocean research. In this paper, the simplified two-dimensional (2D) storm surge governing equation is introduced into an ANN to establish a PINN-based storm surge forecast model. The numerical simulation results of 14 storm surge events in the Bohai Sea are selected as the PINN training set, and 6.3% of the training set data are randomly selected to reconstruct the storm surge field information. The storm surge reconstructed at each tide station is nearly identical to the storm surge curve simulated by the numerical model, with the root mean square error (RMSE) less than 0.12 m and absolute error of maximum storm surge less than 0.2 m. The analysis of the storm surge field at key moments (storm surge height lager than 1 m) shows that the difference in storm surge field between the PINN reconstruction and the numerical model is generally less than 0.4 m. Two storm surge events in the Bohai Sea are selected as forecast cases, and the same network structure, parameters, and storm surge data assimilation scheme are used for predictions by the ANN, PINN, and numerical model. The results show that compared to the ANN and numerical models, the average relative error of the maximum storm surge predicted by the PINN is reduced by approximately 25%, which significantly improves the forecast accuracy, therefore, the PINN is suitable for storm surge forecasting and research due to its advantages in small sample data training and strong physical meaning.

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Principle of Machine Learning and Its Potential Application in Cli-mate Prediction

Cited by 6 publications

References 0 publications

Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

Forecasting the June Ridge Line of the Western Pacific Subtropical High with a Machine Learning Method

Extended-Range Forecast of Regional Persistent Extreme Cold Events Based on Deep Learning

Storm surge forecasting based on physics-informed neural networks in the Bohai Sea

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