“Old-school physics-based weather forecasting and even new AI models typically become less reliable beyond two weeks, a problem Microsoft claims to have solved using machine learning…

Microsoft calls its method Probabilistic Bias Correction (PBC). The tool uses machine learning to identify systematic bias in a model and make corrections. One example Mackey gives is to correct for changes in climate. Greenhouse gas emissions from fossil fuels are heating up the planet, but a weather model might make predictions based on historical records that can bias it toward colder temperatures than the world is actually experiencing today.

The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets…

The amount of compute and infrastructure needed for weather forecasting has often left technological advances out of reach for many agencies in economically developing countries. The paper’s authors, however, think PBC can be different. 'One of the things that’s really exciting to me about models like PBC is how lightweight they are, how easy to train, that scientists without huge pools of resources can work on trying to configure [these] models and make them perform better in specific localities,' says Genevieve Flaspohler, one of the authors of the paper.”

From The Verge.