Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms

Understand

Numerical weather forecasting using high-resolution physical models often requires extensive computational resources on supercomputers, which diminishes their wide usage in most real-life applications.

• As a remedy, applying deep learning methods has revealed innovative solutions within this field.
• To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data.
• Our approach extends the conventional encoder-decoder structure by integrating Convolutional Long-short Term Memory and Convolutional Neural Networks.