*2.3. Methodology*

With the goal of answering our first research question (RQ1), we developed and evaluated our *NeXtNow* deep learning model, which was customized for the short-term prediction of weather radar products. *NeXtNow* was adapted for radar data prediction from the ResNeXt [17] architecture, which is mainly used for image processing. To the best of our knowledge, no other architecture that is based on ResNeXt has been proposed for weather nowcasting or spatiotemporal prediction problems. While several works have proposed fully convolutional or convolutional–recurrent neural networks for weather forecasting, they have employed simple and causal 2D or 3D convolutional architectures [34] and architectures that were inspired by U-Net [24] or the Xception model [19]. The basic details of the ResNeXt deep learning model are presented in Section 2.3.1, then Section 2.3.2 introduces the model that was used in our approach. Our *NeXtNow* learning model is introduced in Section 2.3.3, while the testing stage of *NeXtNow* and the methodology that was employed for the performance evaluation is discussed in Section 2.3.4.
