Multi-Dilated Convolutional LSTM With U-Net for Global Sea Surface Temperature Forecasting

Abstract

Accurate forecasting of sea surface temperature (SST) is pivotal for a wide range of applications ranging from climate modelling to marine ecosystem management. This study introduces an ingenious multi-dilated model that employs dilated Convolutional Long Short-Term Memory (ConvLSTM) networks alongside a U-Net architecture which aims to enhance the precision of monthly SST predictions at lead periods of 3, 6 and 12 months. Our approach innovatively combines dilated convolutions within ConvLSTM with the segmentation capabilities of U-Net to adeptly capture the complex Spatio-temporal dynamics as well as relevant attributes of SST. The study utilizes a high-resolution MPI-ESM1-2-HR SST dataset for a series of rigorous tests, achieving a Mean Square Error of 0.01, indicating a high accuracy level in SST predictions. The model at a lead time of 12 months also showed a Sea Surface Microwave Index of 0.036, illustrating its effectiveness in reflecting SST’s microwave emissivity characteristics, and an Earth Mover’s Distance score of 0.97, highlighting its ability to closely match the predicted SST distribution with the actual one. Furthermore, a cosine similarity score of 0.99 suggests a significant alignment between the predicted and actual SST patterns. By merging multi-dilated convolutions with segmentation, our model tackles the intricate challenge of simultaneously capturing the spatial and temporal dependencies of SST, setting a new standard for forecasting accuracy in the field.

Keywords

• Multi-dilation
• segmentation
• U-Net
• spatio-temporal
• sea surface temperature
• CNN