Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM

Li, Chen; Sun, Tianying; Feng, Yuan; Zhang, Xingzhi

doi:10.3390/rs14030523

Cited by 17 publications

(4 citation statements)

References 24 publications

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“…Eighth, Li et al [40] applied a convolutional LSTM (convLSTM) neural network to calculate the long-term Indian Ocean Dipole (IOD) index by predicting the sea surface temperature (SST) in the next seven months. Based on the analysis of complex temporal and spatial relationships among marine atmospheric data, the wind field signal information of the physical ocean was proposed to predict the IOD phenomenon from the combination of prior knowledge of the physical ocean with the deep learning method.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

et al. 2022

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Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.

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Section: Overview Of Contributionsmentioning

confidence: 99%

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

et al. 2022

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“…Recent studies suggest that the Indian Ocean Dipole (IOD) plays an important role in predicting the ENSO in the tropical Pacific through teleconnections (Li et al, 2022; Liu et al, 2023). The positive IOD events are associated with reduced rainfall over western and southern Australia (Zhao et al, 2019), enhanced rainfall in eastern and southern Africa (Black et al, 2003), and reduced rainfall over central and southeastern Brazil and enhanced rainfall over the Amazon (Sena & Magnusdottir, 2021).…”

Section: Introductionmentioning

confidence: 99%

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

Phakula,

Landman,

Engelbrecht

2024

Meteorological Applications

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Subseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters.

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“…Based on this, Zhang et al [38] proposed a multilayer superposed ConvLSTM (M-ConvLSTM) structure to predict 3D ocean temperature, which is able to obtain ocean temperature from horizontal and vertical directions for different depth layers. Chen et al [39] used a ConvLSTM network to predict the Indian Ocean dipole (IOD) to study climate change and other oceanic phenomena. Various LSTMs were also derived, such as Regional Convolution LSTM (RC-LSTM) [40], Multivariate Convolutional LSTM (MVC-LSTM) [41], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, the SST prediction was mainly focused on special sea areas, such as the coastal areas of China [42][43][44], the Indian Ocean [39,[45][46][47], and the Black Sea [48]. However, there are fewer studies on global SST prediction using deep learning networks.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks

Tian-liang

Zhou

et al. 2023

JMSE

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The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

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Long Term Indian Ocean Dipole (IOD) Index Prediction Used Deep Learning by convLSTM

Cited by 17 publications

References 24 publications

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere

Short-Term Prediction of Global Sea Surface Temperature Using Deep Learning Networks

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