Deep Learning for Oceanography
LEVERAGING AN ATMOSPHERIC FOUNDATIONAL MODEL FOR SUBREGIONAL SEA SURFACE TEMPERATURE FORECASTING
This study successfully adapts Aurora, a foundational deep learning model for atmospheric forecasting, to predict sea surface temperature (SST) in the Canary Upwelling System. By fine-tuning the model with high-resolution oceanographic reanalysis data, it achieves a low RMSE of 0.119K and high anomaly correlation coefficients (ACC ≈ 0.997). The approach demonstrates the feasibility of cross-domain knowledge transfer for oceanic applications, highlighting its potential for improved climate modeling and ocean prediction accuracy, despite challenges in capturing finer coastal details.
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0.000K
Achieved RMSE (Kelvin)
0.000
Anomaly Correlation Coeff.
Significant
Computational Cost Reduction
Deep Analysis & Enterprise Applications
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0.119K
Achieved RMSE for SST Prediction
Enterprise Process Flow
Adapt Aurora Model
→
Fine-tune with Reanalysis Data
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Predict SST (Canary Upwelling System)
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Evaluate Performance
| Setting | RMSE (K) | Bias (K) | ACC |
|---|---|---|---|
| Full fine-tuning, lr = 1e-5, batch=3 | 0.131 | -0.069 | 0.997 |
| Decoder fine-tuning, lr = 1e-4, batch=3 | 0.140 | -0.064 | 0.997 |
| Full fine-tuning with new decoder, lr = 1e-5, batch=3 | 0.124 | -0.064 | 0.997 |
| Full fine-tuning, lr = 1e-5, batch=8 | 0.130 | -0.062 | 0.997 |
| Decoder fine-tuning, lr = 1e-4, batch=8 | 0.135 | -0.059 | 0.997 |
| Full fine-tuning with new decoder, lr = 1e-5, batch=8 | 0.134 | -0.033 | 0.997 |
Challenges in Coastal Region Forecasting
The model reveals more errors in coastal zones due to their inherent complexity and intense temperature variations. Factors like ocean currents, coastal topography, and local winds generate microclimates, making predictions challenging.
In contrast, open ocean conditions are more uniform, leading to reduced prediction errors. This suggests that specific training focused on coastal areas, potentially with high-resolution data or physics-informed neural networks, could significantly improve overall model performance.
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