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- Title
A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting.
- Authors
Wang, Guosong; Wang, Xidong; Wu, Xinrong; Liu, Kexiu; Qi, Yiquan; Sun, Chunjian; Fu, Hongli
- Abstract
The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.
- Subjects
SEA level; DEEP learning; HILBERT-Huang transform; OCEAN temperature; REMOTE sensing; WIND speed
- Publication
Journal of Atmospheric & Oceanic Technology, 2022, Vol 39, Issue 3, p285
- ISSN
0739-0572
- Publication type
Article
- DOI
10.1175/JTECH-D-21-0043.1