Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea

Shao, Qi; Hou, Guangchao; Li, Wei; Han, Guijun; Liang, Kangzhuang; Bai, Yang

doi:10.1029/2020ea001558

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…To acquire a long-range forecast of the SCS SST, we develop hybrid models by combining the traditional EOF analysis [13] and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [5,6,14] with each of the three deep neural networks of BP, LSTM, and GRU. BP is the most efficient and widely used algorithm in deep learning [15].…”

Section: Hybrid Modelmentioning

confidence: 99%

“…As for the site-independent category, although spatial correlation can be effectively taken into account, the daily SST forecast skill horizon length is usually restricted, and often shorter than 10 days [4]. To overcome the shortcomings of the prediction models in the literature and adapt to the characteristics associated with geo-scientific analysis, traditional time series techniques such as empirical orthogonal function (EOF) analysis and empirical mode decomposition (EMD) are introduced to develop hybrid artificial intelligence models [5,6]; in doing so, the skillful daily SST forecast range over climatology can be extended up to 30 days. The question now is to what extent deep learning-based prediction models have skills in forecasting SST, which are comparable to or better than current forecast horizon achieved by the state-of-the-art global operational forecasting system, namely the Navy Earth System Prediction Capability (Navy-ESPC) fully coupled atmosphere-ocean-sea ice prediction system developed for subseasonal forecasting at the U.S.…”

Section: Introductionmentioning

confidence: 99%

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SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

Zhang,

Han,

et al. 2024

Remote Sensing

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We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin.

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Section: Hybrid Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

Zhang,

Han,

et al. 2024

Remote Sensing

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“…These are the problems that do not have existing accepted solutions, rely heavily on judgments of highly experienced experts, yet could lead to the most profound scientific insights if investigated properly. A few studies explore the promise of ML in multi‐disciplinary data integration for predicting drought behavior in the Colorado River Basin based on various Earth System Models (Talsma et al., 2022), for predicting sea surface variabilities in the South China Sea (Shao et al., 2021), for geothermal heat flow prediction from multiple geophysical and geological datasets (Lösing & Ebbing, 2021), for identifying volcano's transition from non‐eruptive to eruptive states (Manley et al., 2021), for understanding the geodynamic history using geochemical data (Jorgenson et al., 2022; X. Lin et al., 2022; X. Li & Zhang, 2022; Saha et al., 2021; Thomson et al., 2021; Y. Wang et al., 2021), and for characterizing geodetic signals by their sources (Hu et al., 2021). Albert (2022) uses an unsupervised deep NN structure to predict future atmospheric structures from past measurements to enable infrasound propagation modeling.…”

Section: Highlightsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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“…To improve the performance of the CEOF-LSTM model, we add the BEMD analysis to the prediction model (Rilling et al, 2007;Shao et al, 2021a), called the CEOF-BEMD-LSTM model. Figure 6 shows the framework of the CEOF-BEMD-LSTM model, which can be broken down into three parts: (A) data preprocessing, (B) LSTM prediction, and (C) correction.…”

Section: Prediction Experiments Using Ceof-bemd-lstm Modelmentioning

confidence: 99%

A hybrid deep learning model for predicting the Kuroshio path south of Japan

Han

et al. 2023

Front. Mar. Sci.

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At present, many prediction models based on deep learning methods have been widely used in ocean prediction with satisfactory results. However, few deep learning models are used to predict the Kuroshio path south of Japan. In this study, a hybrid deep learning prediction model is constructed based on the long short-term memory (LSTM) neural network, combined with the complex empirical orthogonal function (CEOF) and bivariate empirical mode decomposition (BEMD), called CEOF-BEMD-LSTM. We train the model by using a 50-year (1958-2007) long time series of daily mean positions of the Kuroshio path south of Japan extracted from a regional ocean reanalysis dataset. During the test period of 15 years (2008-2022) by using daily altimetry dataset, our model shows a good performance for the Kuroshio path prediction with the lead time of 120 days, with 0.44° root-mean-square error (RMSE) and 0.75 anomaly correlation coefficient (ACC). This model also has good prediction skill score (SS). Moreover, the CEOF-BEMD-LSTM model successfully hindcasts the formation of the latest Kuroshio large meander since the summer of 2017. Predictions of the Kuroshio path for the coming 120 days (from January1 to April 30, 2023) indicate that the Kuroshio will continue to remain in the state of the large meander. Besides, predictor(s) of the Kuroshio path south of Japan need to be sought and added in future research.

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Ocean Reanalysis Data‐Driven Deep Learning Forecast for Sea Surface Multivariate in the South China Sea

Cited by 17 publications

References 31 publications

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

SST Forecast Skills Based on Hybrid Deep Learning Models: With Applications to the South China Sea

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

A hybrid deep learning model for predicting the Kuroshio path south of Japan

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