Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is uncertain. To investigate the applicability of ERA5 during hurricanes, this study used buoy data as reference values and assessed the applicability of ERA5 sea-surface wind speed (WS), sea-surface temperature (SST), and sea-surface pressure (SSP) during the 2020 Atlantic hurricane “ISAIAS” through spatial distribution and error analysis. The results indicate that there is a positive correlation and consistency between the trends of ERA5 and reference values. The average correlation coefficients for SSP, WS, and SST are 0.953, 0.822, and 0.607, respectively. Nearshore topography has a significant impact on data accuracy, resulting in greater errors compared to open-water areas. This study provides a theoretical basis for the application of ERA5 data during hurricanes.

Marine heatwaves (MHWs) have been shown to exert a substantial influence on marine ecosystems and associated industries. Consequently, the development of accurate prediction models is imperative for mitigating ecological risks. This study concentrates on the East China Sea, employing sea surface temperature (SST) data from the OISST v2.1 dataset, which spans from 1982 to 2024, for the purpose of examining the spatial and temporal characteristics of six significant MHW indicators. The results reveal a clear annual increase in all six indicators. This study employed the deep learning-based SegRNN_ST model to forecast future MHW trends. It integrated a spatiotemporal attention mechanism and was optimized using mean absolute error (MAE) and mean squared error (MSE) as loss functions. And the coefficient of determination (R²) was utilized as a performance metric for predicting MHWs in the East China Sea. The findings indicate that the improved SegRNN_ST model increased the R² by 8% compared to the original model, with MSE and MAE showing reductions of 20% and 15%, respectively. This study demonstrates fewer errors than SegRNN and enhanced accuracy in predicting MHWs. This study introduces a new method for predicting and providing early warnings of MHWs, improving the accuracy of forecasts for extreme marine weather events.