Deep Learning-based Extreme Heatwave Forecast

Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. Physics driven weather forecast systems or climate models can be used to forecast their occurrence or predict their probability. The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast extreme heatwave occurrences. This new approach will be useful for several key scientific goals which include the study of climate model statistics, building a quantitative proxy for resampling rare events in climate models, study the impact of climate change, and should eventually be useful for forecasting. Fulfilling these important goals implies addressing issues such as class-size imbalance that is intrinsically associated with rare event prediction, assessing the potential benefits of transfer learning to address the nested nature of extreme events (naturally included in less extreme ones). We train a Convolutional Neural Network, using $1000$ years of climate model outputs, with large-class undersampling and transfer learning. From the observed snapshots of the surface temperature and the $500$ hPa geopotential height fields, the trained network achieves significant performance in forecasting the occurrence of long lasting extreme heatwaves. We are able to predict them at three different levels of intensity, and as early as $15$ days ahead of the start of the event ($30$ days ahead of the end of the event).