GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme
  Precipitation Nowcasting
Eloy Reulen Siamak Mehrkanoon
In recent years, data-driven modeling approaches have gained considerable
traction in various meteorological applications, particularly in the realm of
weather forecasting. However, these approaches often encounter challenges when
dealing with extreme weather conditions. In light of this, we propose
GA-SmaAt-GNet, a novel generative adversarial architecture that makes use of
two methodologies aimed at enhancing the performance of deep learning models
for extreme precipitation nowcasting. Firstly, it uses a novel SmaAt-GNet built
upon the successful SmaAt-UNet architecture as generator. This network
incorporates precipitation masks (binarized precipitation maps) as an
additional data source, leveraging valuable information for improved
predictions. Additionally, GA-SmaAt-GNet utilizes an attention-augmented
discriminator inspired by the well-established Pix2Pix architecture.
Furthermore, we assess the performance of GA-SmaAt-GNet using real-life
precipitation dataset from the Netherlands. Our experimental results reveal a
notable improvement in both overall performance and for extreme precipitation
events. Furthermore, we conduct uncertainty analysis on the proposed
GA-SmaAt-GNet model as well as on the precipitation dataset, providing
additional insights into the predictive capabilities of the model. Finally, we
offer further insights into the predictions of our proposed model using
Grad-CAM. This visual explanation technique generates activation heatmaps,
illustrating areas of the input that are more activated for various parts of
the network.