**5. Conclusions**

In this paper, we proposed a convolutional deep learning model called *NeXtNow*, which was inspired by the ResNeXt architecture for weather radar data forecasting. *NeXtNow* adapted the ResNeXt [17] architecture that has been proposed in the computer vision literature for the task of spatiotemporal prediction. Our proposed model has an encoder– decoder architecture, which maps past radar measurements onto radar measurements that are recorded in the future. We noted the generality of the *NeXtNow* model, which was proposed for short-term radar data prediction. The model could be applied not only to nowcasting but also to predicting other meteorological phenomena, such as heatwaves or droughts.

To evaluate the performance of *NeXtNow* using radar data that were obtained from different geographical/climatic areas and contained different radar measurements, two case studies were considered: one using data that were collected from Romania and the other employing data that were collected from Norway.

The research questions that were formulated in Section 1 were answered. The *NeXtNow* model that was designed for short-term radar data prediction answered RQ1. To the best of our knowledge, the ResNeXt architecture has ye<sup>t</sup> not been adapted for the task of spatiotemporal prediction or, more specifically, radar data prediction. RQ2 was answered by the experiments that were performed using the time series of radar data that were provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute. We empirically showed through these case studies that including multiple past radar measurements did not improve predictions for one time step in the future, but they did provide more accurate predictions for multiple time steps further in the future. Our experimental evaluation of *NeXtNow* also highlighted an improvement in performance when predicting the values of the radar products based on their historical values compared to the performance of a convolutional architecture that has been previously proposed in the nowcasting literature for short-term prediction of radar data (*XNow* [19]).

To answer to RQ3, the improvement in the performance of *NeXtNow* with respect to that of *XNow* was proven to be statistically significant, with a significance level of *α* = 0.01, as shown by a one-tailed paired Wilcoxon signed-rank test. Additionally, through our experiments, we empirically showed that including multiple past radar measurements led to more accurate predictions at time steps that were further in the future.

The *NeXtNow* architecture that was proposed in this paper offers one step toward the broader goal of our research, which is to develop accurate ML-based prediction models that can be integrated into both Romanian and Norwegian weather nowcasting systems.

Future work will be carried out with the aim of improving the predictive performance of our model for extreme weather phenomena by using weighted loss functions, which could emphasize the errors that are obtained for high reflectivity values. Comparisons between our *NeXtNow* model and other classic methods for radar echo extrapolation are also envisaged for a more thorough experimental validation. For instance, advection methods (e.g., Lagrangian advection), which use reflectivity values from multiple past time steps as inputs, are known for their very good performances and may offer comparable or even better performances than *NeXtNow* by providing sharper predictions. Nevertheless, the spatial resolution of our model's predictions could be enhanced by using perceptual losses or adversarial training techniques, which will also be investigated in future work. Data that are collected from geographical areas other than Romania and Norway will be used to further validate the *NeXtNow* model. Future performance improvements are also envisaged for predicting multiple times steps in the future by extending our approach to include a recurrent architecture.

**Author Contributions:** Conceptualization, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; methodology, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; software, A.-I.A. and A.M. (Andrei Mihai); validation, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; formal analysis, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; investigation, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; resources, A.-I.A., G.C., A.M. (Andrei Mihai), I.G.C., S.B. and A.M. (Abdelkader Mezghani); data curation, S.B.; writing— original draft preparation, G.C.; writing—review and editing, A.-I.A., G.C., A.M. (Andrei Mihai), S.B. and A.M. (Abdelkader Mezghani); visualization, A.-I.A., G.C., A.M. (Andrei Mihai) and I.G.C.; funding acquisition, G.C., S.B. and A.M. (Abdelkader Mezghani). All authors have read and agreed to the published version of the manuscript.

**Funding:** The research leading to these results has received funding from the NO Grants 2014–2021 under project contract number 26/2020.

**Data Availability Statement:** The data that were used in this study are available at [41] (NMA data) and [33] (MET data).

**Acknowledgments:** The authors would like to thank the editors and anonymous reviewers for their useful suggestions and comments that helped us to improve this paper and presentation. The research leading to these results has received funding from the NO Grants 2014–2021 under project contract number 26/2020.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, the collection, analyses or interpretation of data, the writing of the manuscript or the decision to publish the results.
