Integrating Prithvi WxC with a Hurricane Intensity Estimation Model for Accurate Hurricane Forecasting

Accurate hurricane intensity estimation is critical for disaster preparedness, yet remains challenging for weather models trained on coarse-resolution datasets. This study proposes a hybrid approach that integrates NASA-IBM's Prithvi WxC model with a deep learning-based Hurricane Intensity Estimation (HIE) model. While the Prithvi WxC model excels in global atmospheric predictions, its coarse-grained outputs can struggle with precise hurricane intensity estimation. To address this, the HIE model is triggered when it identifies a hurricane in the Prithvi model output, providing corrected intensity predictions based on high-resolution data.

A dataset was created for training and evaluation, consisting of 6,000 unique initial conditions from 1980 to 2024 that resulted in hurricanes across all major basins. Ground truth hurricane tracks and intensity data were obtained from the HURDAT database The training phase focused on hurricane cases from 1980 to 2000, building a foundational understanding of global hurricane characteristics. Subsequently, the model was fine-tuned with 2000–2020 data to account for basin-specific variations and improve regional accuracy. The remaining cases (2020–2024) are reserved for validation and assessment. The HIE model employs advanced deep learning techniques to refine key intensity metrics, such as maximum sustained wind speeds and central pressure. By addressing the limitations of Prithvi WxC's coarse-resolution training data, the HIE model achieves greater precision, leveraging fine-grained atmospheric and oceanographic features. This two-step framework, hurricane detection by Prithvi WxC followed by intensity refinement by the HIE model, capitalizes on the strengths of both models to deliver improved predictions.

This highlights the potential of combining foundation models like Prithvi WxC with specialized deep-learning frameworks to overcome existing limitations in hurricane intensity estimation. By incorporating diverse data sources and leveraging modern machine-learning techniques, this hybrid approach bridges the gap between coarse-grained global models and the need for precise regional forecasting.