Intense precipitation can have extensive economic outcomes, from disrupting outdoor activities to causing severe infrastructural damage, such as landslides, and endangering public safety. The urgency to mitigate these impacts underscores the need for improved early warning systems. Enhanced short-term weather prediction, or nowcasting, is critical for addressing these severe weather events effectively. Traditional meteorological forecasting methods, while foundational, are often constrained by simplistic physical assumptions and fail to capture the complex, nonlinear patterns of intense weather events. These methods also struggle with high computational demands and lack the resolution needed to detect crucial microscale atmospheric phenomena for accurate short-term forecasts.
To address these challenges, this research introduces a novel deep learning approach utilizing a streamlined architecture that combines a Vector Quantized Variational Autoencoder (VQVAE) and an Autoregressive (AR) Transformer. This model aims to predict weather conditions up to 180 minutes ahead, using data analyzed at 30-minute intervals. The proposed model displays comparable performance with the state-of-theart conventional methods and other deep learning nowcasting models in predicting precipitations and sometimes extreme events. This study seeks to enhance forecasting accuracy and efficiency, providing valuable contributions to the field of meteorological nowcasting.