Sparse-data method estimates sea surface temperatures 40% more accurately than conventional approach

Researchers have developed a method for extrapolating sea surface temperatures from sparse data that is significantly more accurate than other commonly used computational methods and slightly more accurate than the best-performing AI model, while taking a fraction of the time to train. The work has implications for both short-term weather forecasting and longer-term climate predictions.

Read it at Phys.org ↗

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