MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales.
Abstract: Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales ...
Hou et al. [36] proposed an encoderdecoder model (MIMO) that is capable of learning spatio-temporal features from SST data at multiple scales and fusing ...
Jul 8, 2024 · MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction by Siyun Hou, et al. ➡️ https://t.co/RdRwtnWVdV.
MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction ... Multi-Out (MIMO) model to predict SST at different scales.
The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach ...
The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and ...
This study has examined the quality of SST forecasts issued by a superensemble constructed from 13 state-of-the-art coupled atmosphere–ocean models. It was ...
TL;DR: This work proposes a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales and achieves the ...
Accurate prediction of sea surface temperature is important for coping with climate change, marine ecological protection, and marine economic development. In ...