Improving extreme weather events detection with light-weight neural networks
This work addresses the detection of extreme weather events like tropical cyclones, which is crucial for climate change adaptation, but it is incremental as it builds on existing neural network methods with specific optimizations.
The paper tackled the problem of improving automated detection of tropical cyclones in climate data by modifying a light-weight neural network architecture, achieving success through weighted loss functions to enhance recall and address class imbalance for these rare events.
To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data. Our primary focus is on tropical cyclones, the most destructive weather events, for which current models show limited performance. We investigate feature engineering, data augmentation, learning rate modifications, alternative loss functions, and architectural changes. In contrast to previous approaches optimizing for intersection over union, we specifically seek to improve recall to penalize under-counting and prioritize identification of tropical cyclones. We report success through the use of weighted loss functions to counter class imbalance for these rare events. We conclude with directions for future research on extreme weather events detection, a crucial task for prediction, mitigation, and equitable adaptation to the impacts of climate change.