Survey on the Application of Deep Learning in Extreme Weather Prediction
In one sentence. Survey of deep learning methods, including RNNs and CNNs, for predicting extreme weather events.
Abstract
Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework.
Labels
- Topic
- Extremes & hazards
- Type
- Review, survey or tutorial
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- 6 h–3 days
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/atmos12060661 OpenAlex:W3160662458
- Added
- 2026-10-22
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