# Improving extreme weather events detection with light-weight neural networks Source: [Improving extreme weather events detection with light-weight neural networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:10:39.531Z Authors: Romain Lacombe, Hannah Grossman, Lucas Hendren, David L\\"udeke (1) \_\_INSTITUTION\_4\_\_ Stanford University, (2) Plume Labs) Published: 2023-03-31T23:38:54.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()