Traditional models still outperform AI for extreme weather forecasts
A new study finds that AI-based climate models underperform compared to traditional physics-based models in forecasting record-breaking extreme weather events. While AI models are faster and often accurate for typical conditions, they struggle with unprecedented extremes due to reliance on historical data. Researchers caution against rapidly replacing traditional models with AI for extreme weather prediction.
- ▪AI models underestimated both the frequency and intensity of record-breaking hot, cold, and windy events in 2018 and 2020.
- ▪Traditional physics-based models, such as the European Centre's High RESolution forecast model, remain more reliable for simulating unprecedented extreme weather.
- ▪AI models depend heavily on historical training data, limiting their ability to predict novel or record-shattering weather events.
- ▪The study analyzed over 240,000 record-breaking weather events identified using ERA5 reanalysis data from 1979 to 2020.
- ▪Researchers emphasize that physics-based models are still essential for accurate early warning systems amid increasing climate extremes.
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Computer models that use artificial intelligence (AI) cannot forecast record-breaking weather as well as traditional climate models, according to a new study. It is well established that AI climate models have surpassed traditional, physics-based climate models for some aspects of weather forecasting. However, new research published in Science Advances finds that AI models still “underperform” in forecasting record-breaking extreme weather events. The authors tested how well both AI and traditional weather models could simulate thousands of record-breaking hot, cold and windy events that were recorded in 2018 and 2020. They find that AI models underestimate both the frequency and intensity of record-breaking events.
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