Sea Surface Temperature

Sea surface temperature (SST) is a crucial climate variable, and research focuses on accurately predicting its spatial and temporal dynamics for improved weather forecasting and climate change understanding. Current efforts utilize advanced machine learning models, including deep learning architectures like transformers, convolutional GRUs, and implicit neural networks, often incorporating data assimilation techniques and leveraging spatial correlations to enhance prediction accuracy, particularly in data-sparse regions. These improved SST predictions have significant implications for various sectors, including marine ecosystems management, resource allocation, and mitigating the impacts of extreme weather events.