A Deep Learning Model for Precipitation Nowcasting Using Data Fusion

Climate change is intensifying precipitation events, leading to increased flooding and landslides, particularly in urban areas. Traditional Numerical Weather Prediction (NWP) models are essential for forecasting these events, but require extensive computational resources for high spatio-temporal resolution. Machine Learning (ML) approaches offer a more computationally efficient alternative, with fast prediction phases suitable for less powerful resources, while intensive training can be done asynchronously. We propose a deep learning architecture that incorporates an information data fusion module prior to a CNN-based decision module to reduce the input size while maintaining prediction accuracy. By testing the application of two different subspace methods, Principal Component Analysis decomposition (PCA) and Maximum pixel map, we reduce the number of input channels needed for the decision module. Experiments on the rainfall dataset compare the performance of the decision module with and without the fusion module. The experiments conclude that while the baseline original SmaAt-UNet remains the most reliable, the Fusion Module Max Pixel Map provides a substantial input compression while maintaining performance. This could enable the execution of forecasts on devices with reduced processing capabilities, such as mobile devices.