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MIT researchers developed a machine-learning approach that improved weather forecasts for the two-to-six-week window, setting records for this critical subseasonal timeframe used in emergency planning.

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MIT Researchers Improve Weather Forecasts Two to Six Weeks Out

What happened?

FAQ

What is subseasonal forecasting and why does it matter?

Subseasonal forecasting covers the two-to-six-week window, a difficult period to predict but crucial for decisions like water management and disaster preparedness.

How does this approach differ from traditional weather models?

The new approach relies on machine learning rather than traditional physics-based models, achieving higher accuracy in this challenging timeframe.

Can MENA countries benefit from this technology?

Yes, it could support planning for drought, floods, and heatwaves, though adoption requires local data and computing infrastructure.

Source: MIT News (AI)

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