Deep Learning-based Extreme Heatwave Forecast

Abstract

Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. We specifically study long-lasting extreme heat waves, which are among the most important for climate impacts. 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 the occurrence of extreme long-lasting heatwaves. 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).

arXiv comment21 pages, 5 figures

References in corpus (12)

• On the Convergence of Adam and Beyond
• Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
• Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets
• Analog forecasting of extreme-causing weather patterns using deep learning
• Computation of extreme heat waves in climate models using a large deviation algorithm
• Rare event algorithm study of extreme warm summers and heat waves over Europe
• Fingerprinting Heatwaves and Cold Spells and Assessing Their Response to Climate Change using Large Deviation Theory
• Applications of large deviation theory in geophysical fluid dynamics and climate science
• Efficient Object Localization Using Convolutional Networks
• Computation of extremes values of time averaged observables in climate models with large deviation techniques
• A Large Deviation Theory-based Analysis of Heat Waves and Cold Spells in a Simplified Model of the General Circulation of the Atmosphere
• A Capacity Scaling Law for Artificial Neural Networks

Cited by in corpus (2)

• Global Extreme Heat Forecasting Using Neural Weather Models
• The Typicality of Regimes Associated with Northern Hemisphere Heatwaves