Al to Better Predict Local Extreme Events.

• Position: WEATHER FORECASTS

A critical forecasting challenge--predicting local extreme weather events--has been solved by ClimateAi researcher Stephan Rasp and Han Price of the University of Oxford, who have created an innovative machine-learning approach using generative adversarial networks (GANs) trained on global weather forecasts to correct for the biases that exist in current models.

"Going beyond the recent flurry of activity to improve 'nowcasts,' our new model achieves that same accuracy for forecasts on the horizon of several hours to days," notes Rasp, lead data scientist for ClimateAi. "We can account for systematic errors in the global models and increase the resolution of the forecast so that regional extremes are accurately captured."

While a warming, wetter world due to climate change makes weather extremes more frequent and intense, developing accurate regional forecasts is notoriously difficult because of the complex physics driving heavy precipitation and extreme weather events.

Global forecasts can leverage the availability of a great deal of data and weather models, but lack precision and are prone to errors because any small unaccounted for detail can cause divergence on a large scale. Regional forecasting, on the other hand, requires expensive and time-consuming supercomputers with trained local practitioners, limiting access to rich countries.

The new model downscales global forecasts to be as accurate as a local forecast, without requiring the vast amounts of computational, financial, and human resources previously needed for such a small scale. Offering accurate local forecasts for precipitation and extreme weather without the traditional (and costly) constraints of current forecasting systems, these findings could provide a new paradigm for forecasting extremes in low-income countries that cannot afford the technology for high-resolution local forecasts.

By training GANs--a subset of machine learning in which two neural networks basically fight and train each other until they arrive at a conclusion--to first look at coarse global weather forecasts and correct for errors, then to downscale the...