Researchers from the Massachusetts Institute of Technology (MIT) have developed a machine learning system for improved climate forecasting. The technology, which won the Sub-Seasonal Climate Forecast Rodeo competition, is designed to make predictions of temperature and precipitation anomalies up to six weeks before they occur. In their paper, published in Nature Communications, researchers describe how the algorithm learns about weather patterns by analyzing data collected over time using sensors on satellites that measure temperature, humidity, wind speed and other factors.
Researchers from the Massachusetts Institute of Technology (MIT) have developed a machine learning system for improved climate forecasting. The technology, which won the Sub-Seasonal Climate Forecast Rodeo competition, is designed to make predictions of temperature and precipitation anomalies up to six weeks before they occur. The MIT team combined historical weather-pattern recognition with machine learning techniques, and managed to outperform the US government’s operational long-range forecasting system.