Weather forecasts can miss rare events like the extreme rainfall caused by 2017's Hurricane Harvey Training AI models on historical weather patterns can turn them into accurate forecasters – but they may not be able to predict extreme events that don't occur in their training data. This could be a growing issue as climate change drives more unprecedented weather. "These models are good, but the question we have been asking is about events that are so rare and strong that these models…

Getting caught out in the rain might soon be a thing of the past thanks to a powerful new AI weather forecaster. Google DeepMind has unveiled an AI-powered weather model called GenCast which it claims is faster and more accurate than traditional forecasts. Compared to the top-performing supercomputer Google's GenCast model was more accurate across 99.8 per cent of predictions up to 15 days in advance. According to Google, this will not only help commuters decide whether to bring an umbrella but also spot natural disasters like Typhoons before it is too late. Normally, weather agencies like the Met Office predict the weather by using huge supercomputers to crunch the complex maths which simulates the climate.

Thanks to a new partnership with the Alan Turing Institute, National Grid Electricity System Operator (ESO) announced it has developed new AI prediction models that have improved solar forecasting by one-third. Knowing how much power will be flowing into the grid on any given day is becoming increasingly crucial as the proportion of intermittent renewable power serving the grid goes up. Rob Rome, commercial operations manager at the ESO, said the new forecast models means the power system can become much more efficient at managing supply and demand. Improved solar forecasts will help us run the system more efficiently, ultimately meaning lower bills for consumers. National Grid worked with researchers and doctoral students at the Institute to develop the improved forecasting models.