Microsoft’s research introduces machine learning corrections for longer-range weather forecasts

What happened

Microsoft researchers and collaborators published research in Nature on Probabilistic Bias Correction (PBC), an open-source machine learning tool that corrects systematic bias in existing weather models to improve forecasts two to six weeks ahead. In the study, PBC improved ECMWF’s physics-based model accuracy for 98 percent of precipitation and 92 percent of temperature targets, and doubled the accuracy of its AI forecasting system. The tool can run on CPUs, though ECMWF cautioned that operational implementation remains challenging.

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The story so far

Microsoft researchers and collaborators published Probabilistic Bias Correction (PBC), an open-source machine learning tool that improved weather forecasts two to six weeks ahead in testing, though ECMWF cautioned that putting it into operational use remains challenging. The study, published in Nature, found that correcting systematic bias in existing weather models improved ECMWF’s physics-based model accuracy for 98 percent of precipitation targets and 92 percent of temperature targets, and doubled the accuracy of its AI forecasting system. PBC can run on CPUs. Microsoft CEO Satya Nadella highlighted the research’s potential to help people prepare for extreme weather.

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