  In recent years, data-driven modeling approaches have gained significant
attention across various meteorological applications, particularly in weather
forecasting. However, these methods often face challenges in handling extreme
weather conditions. In response, we present the GA-SmaAt-GNet model, a novel
generative adversarial framework for extreme precipitation nowcasting. This
model features a unique SmaAt-GNet generator, an extension of the successful
SmaAt-UNet architecture, capable of integrating precipitation masks (binarized
precipitation maps) to enhance predictive accuracy. Additionally, GA-SmaAt-GNet
incorporates an attention-augmented discriminator inspired by the Pix2Pix
architecture. This innovative framework paves the way for generative
precipitation nowcasting using multiple data sources. We evaluate the
performance of SmaAt-GNet and GA-SmaAt-GNet using real-life precipitation data
from the Netherlands, revealing notable improvements in overall performance and
for extreme precipitation events compared to other models. Specifically, our
proposed architecture demonstrates its main performance gain in summer and
autumn, when precipitation intensity is typically at its peak. Furthermore, we
conduct uncertainty analysis on the GA-SmaAt-GNet model and the precipitation
dataset, providing insights into its predictive capabilities. Finally, we
employ Grad-CAM to offer visual explanations of our model's predictions,
generating activation heatmaps that highlight areas of input activation
throughout the network.
