MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST
Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST dat...
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ftmdpi:oai:mdpi.com:/2073-4433/14/9/1358/ 2023-10-01T03:58:05+02:00 MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST Han Peng Wengen Li Chang Jin Hanchen Yang Jihong Guan agris 2023-08-29 application/pdf https://doi.org/10.3390/atmos14091358 eng eng Multidisciplinary Digital Publishing Institute Atmospheric Techniques, Instruments, and Modeling https://dx.doi.org/10.3390/atmos14091358 https://creativecommons.org/licenses/by/4.0/ Atmosphere Volume 14 Issue 9 Pages: 1358 sea surface temperature SST prediction regionality clustering deep learning Text 2023 ftmdpi https://doi.org/10.3390/atmos14091358 2023-09-03T23:53:47Z Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error ... Text North Atlantic South Atlantic Ocean MDPI Open Access Publishing Pacific Atmosphere 14 9 1358 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
sea surface temperature SST prediction regionality clustering deep learning |
spellingShingle |
sea surface temperature SST prediction regionality clustering deep learning Han Peng Wengen Li Chang Jin Hanchen Yang Jihong Guan MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
topic_facet |
sea surface temperature SST prediction regionality clustering deep learning |
description |
Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error ... |
format |
Text |
author |
Han Peng Wengen Li Chang Jin Hanchen Yang Jihong Guan |
author_facet |
Han Peng Wengen Li Chang Jin Hanchen Yang Jihong Guan |
author_sort |
Han Peng |
title |
MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
title_short |
MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
title_full |
MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
title_fullStr |
MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
title_full_unstemmed |
MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST |
title_sort |
mustc: a multi-stage spatio–temporal clustering method for uncovering the regionality of global sst |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/atmos14091358 |
op_coverage |
agris |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
North Atlantic South Atlantic Ocean |
genre_facet |
North Atlantic South Atlantic Ocean |
op_source |
Atmosphere Volume 14 Issue 9 Pages: 1358 |
op_relation |
Atmospheric Techniques, Instruments, and Modeling https://dx.doi.org/10.3390/atmos14091358 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos14091358 |
container_title |
Atmosphere |
container_volume |
14 |
container_issue |
9 |
container_start_page |
1358 |
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1778530516998291456 |