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2 clusters · 4 sources · 3 days · First seen · Last updated

AI development for long-term weather forecasting

Overview

Researchers from MIT and Microsoft have developed an artificial intelligence system designed to improve the accuracy of long-term weather forecasting. Published in the journal Nature, the methodology, known as Probabilistic Bias Correction (PBC), focuses on the two-to-six-week range, a period previously difficult to predict due to atmospheric chaos.

Rather than creating entirely new models, the AI identifies and corrects systematic errors in existing forecasting systems by comparing past inaccuracies with historical data. This approach is intended to be computationally efficient and open-source, allowing organizations with limited resources to utilize more reliable predictions.

These advancements in machine learning are expected to shift disaster management from reactive rescue operations toward proactive prevention. By providing more precise predictions of extreme weather events like floods and hurricanes, the technology aims to mitigate the human and economic costs of climate-driven disasters.

Entities

MIT · Nature · Judah Cohen · Microsoft · Lester Mackey

Timeline

  1. [TECHNOLOGY] 2 sources
    AI technology improves long-term weather forecasting and disaster prevention

    New AI-driven machine learning models are improving the accuracy of sub-seasonal weather forecasts, enabling better prediction of extreme climate events two to six weeks in advance to aid disaster prevention.

  2. [TECHNOLOGY] 2 sources
    Microsoft and MIT researchers develop AI to improve long-term weather forecasting

    New AI technology from MIT and Microsoft researchers has doubled the accuracy of weather forecasts for the two-to-six-week window by correcting systematic errors in existing models.

Sources

focus.it · sapo.pt · socientifica.com.br · stranotizie.it