**1. Introduction**

Short-term weather analysis and forecasting for the next 0 to 6 h, which is known as *weather nowcasting* [1,2], are of grea<sup>t</sup> interest in meteorology due to the increasing number of severe weather events that can severely affect the safety of the human population by causing damage and even mortality. For instance, precipitation nowcasting, which refers to the prediction of rainfall intensity in specific regions in the near future, plays an important role in our daily life [2] and represents a challenging topic in nowcasting. The problem is that short-term weather forecasting is complex and difficult, even for operational meteorologists, mainly due to the large volume of data that needs to be examined and interpreted in a short period of time. In addition, nowcasting [3] is highly dependent on various environmental conditions and requires a lot of human experience.

Significant progress has recently been made in the field of nowcasting, ranging from operational nowcasting systems to the numerous *computational intelligence* solutions that have

**Citation:** Albu, A.-I.; Czibula, G.; Mihai, A.; Czibula, I.G.; Burcea, S.; Mezghani, A. *NeXtNow*: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes. *Remote Sens.* **2022**, *14*, 3890. https://doi.org/10.3390/ rs14163890

Academic Editor: Gwanggil Jeon

Received: 4 July 2022 Accepted: 8 August 2022 Published: 11 August 2022

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been proposed in the literature for detecting the occurrence of severe weather events [4,5]. Radar data [6] are useful for nowcasting [7,8]. Most operational nowcasting systems use numerical weather prediction (NWP) models, which combine radar data and other meteorological observations to provide weather forecasts for up to 6 h in the future, e.g., the INCA system [9]. Still, there are a lot of challenges in issuing precise nowcasting warnings as most severe events (e.g., severe convective storms) occur within small spatial areas and have short overall life cycles. Despite the significant improvements that have been achieved by NWP models in precipitation nowcasting, there are still some limitations to their use, such as insufficient computational resources in operational centers and increased error rates at convection-permitting scales [10].

Most existing operational and semi-operational methods for nowcasting are based on cell-tracking algorithms that use radar data as inputs. A real-time cell-tracking algorithm named TITAN, which is useful for single cells, was introduced by Dixon and Wiener [7] and the SCIT system, which uses a more complex cell-tracking algorithm and reflectivity thresholds, was later proposed by Johnson et al. [8]. An operational nowcasting tool that uses a centroid cell-tracking method (named TRT) was also developed by Hering et al. [11] and used by Meteo Swiss, while Germany's National Meteorological Service uses a nowcasting system that is called NowCastMIX, which was introduced by James et al. [5] and employs fuzzy logic rules to analyze remote and ground observations. Fuzzy logic is also used in a cell-tracking algorithm that was proposed by Jung and Lee [4]. AROME [12] is a prediction model that is used to provide a forecast for up to 30 h in the future and has been used by Meteo France since 2008, while AROME-NWC [13] was later developed for nowcasting in the range of 0–6 h.

*Machine learning* (ML) techniques are useful computational intelligence tools that can assist operational meteorologists in decision-making as they are able to learn relevant patterns from weather-related data. *Deep learning* (DL) [14,15] models have become popular within the ML domain as they are able to express target functions that are more complex than those that are encoded by traditional ML models. Additionally, DL models can automatically extract useful features from raw data, thereby removing the difficult task of manual feature engineering that is required by classical ML models. DL architectures are characterized by a hierarchy of multiple levels of representations. Despite the complex aspects of this type of neural model, the key advantage of DL models is the fact that they are universal approximators, i.e., they have the ability to learn arbitrary functions as a composition of several operations.

Reflectivity (R) and Doppler radial velocity (V) are radar products that are used by operational meteorologists to monitor the spatiotemporal evolution of precipitating clouds and thus, they are useful in weather nowcasting.

These reflectivity and velocity values are also used by operational radar algorithms to estimate rainfall and to track and classify storms: R values that are higher than 35 dBZ [7,16] sugges<sup>t</sup> the possible occurrence of convective storms, which are associated with heavy rainfall. The prediction of the values of these radar products based on their historical measurements is important for the early assessment of storm evolution, which can lead to improved nowcasting and timely severe weather warnings.

Time series data, which represent the values of the selected variables at specific time points, are generally used in forecasting because of the temporal characteristics of these data. This study used time series radar data to predict the values of the radar products in a specific geographical region, based on their historical values. The contribution of the paper is twofold. Firstly, the proposal of *NeXtNow*, which is a new convolutional weather forecasting model that was adapted from 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 that consists of ResNeXt blocks and simple convolutions, which maps past radar measurements onto radar measurements that are recorded in the future. Therefore, *NeXtNow* is a customized version of ResNeXt for the short-term prediction of radar data. We opted for the convolutional architecture instead of

a recurrent alternative due to its training and inference efficiency. The ResNeXt architecture was chosen due to the versatility of ResNet-type [18] architectures, which allows for the design of models that can be customized for specific tasks by stacking multiple blocks of the same type. Moreover, we empirically showed that the inclusion of multiple past radar measurements led to more accurate predictions further in the future. The performance of our proposed *NeXtNow* model was evaluated using two case studies, which consisted of real radar data that were collected from the Romanian National Meteorological Administration (NMA) and the Norwegian Meteorological Institute (MET). The obtained results were analyzed from a meteorological perspective to examine the ability of the *NeXtNow* model to capture relevant patterns in the evolution of radar echoes, i.e., patterns that could be relevant for nowcasting severe weather phenomena. Comparisons between *NeXtNow* and other models in the literature highlighted that *NeXtNow* outperformed a convolutional architecture [19] that was proposed for short-term radar data prediction and that its performance was also comparable to the performances of other similar approaches from the nowcasting literature. To the best of our knowledge, an approach that is similar to *NeXtNow* has not ye<sup>t</sup> been proposed in the nowcasting literature.

To summarize, the following research questions guided this study:


The rest of the paper is organized as follows. Section 2 presents a literature review of recent machine learning and deep learning approaches for weather nowcasting that use radar data (Section 2.1) and an overview of the case studies that were used to evaluate our proposed model (Section 2.2). The methodology that was used in our work is detailed in Section 2.3. Section 3 presents the experimental evaluation of our proposed approach and the comparisons to similar models are presented in Section 4. The last section presents the conclusions of this study and potential directions for future research.
