This did not look guaranteed to work, says Matthew Chantry, machine-learning coordinator at the ECWMF, who is spending this storm season. The algorithms underpinning ChatGPT were trained with trillions of words, largely scraped from the internet, but there’s no sample so comprehensive for Earth’s atmosphere. Hurricanes in particular make up a tiny fraction of the available training data.
Shakir Mohamed, a research director at DeepMind, says that rain and extreme events—the weather events people are arguably most interested in—represent the “most challenging cases,” for AI weather models. There are other methods of predicting precipitation, including a localized radar-based approach developed by DeepMind, but integrating the two is challenging.
Unfortunately, true ensemble forecasts lay out two forms of uncertainty: both in the initial weather observations and in the model itself. AI systems can’t do the latter. This weakness springs from thecommon to many machine-learning systems. When you’re trying to predict the weather, knowing how much to doubt your model is crucial. Lingxi Xie, a senior AI researcher at Huawei, says adding explanations to AI forecasts is the number one request from meteorologists.