This study focuses on nowcasting cloud-to-ground lightning in the Amazon region, utilizing the Green Ocean Amazon (GoAmazon) campaign data. Lightning strikes can have severe socioeconomic consequences, making accurate forecasting systems crucial for risk assessment and decision making. Deep learning models have shown promising results in severe storm forecasting and are widely used in various meteorological applications. Overcoming the challenges posed by the chaotic and highly variable nature of lightning events, different deep learning models were explored for prediction. The performance of the models was compared on the basis of the architecture and data configurations, and the evaluation methods considered spatial variability and sensor accuracy. The study sought to develop a very short-term (up to 12 minutes) forecasting system for cloud-to-ground lightning in the Amazon Basin, taking into account the distinctive characteristics of the event and the uncertainties associated with predicting lightning strikes. Considering various probability thresholds for the occurrence of lightning strikes, the best thresholds were determined for two artificial neural networks, U-Net and ConvLSTM. The validation methods used for model evaluation were also important in this study, given that these methods make validations more assertive due to the consideration of characteristics associated with lightning (e.g., spatial variability) and sensors (e.g., spatial accuracy). Both models tracked lightning effectively, with the best model based on U-Net achieving 0.642 (POD) and 0.353 (FAR), and the best model based on ConvLSTM reaching 0.689 (POD) and 0.307 (FAR). Also, the smallest spatial error (e.g. < 5 km) based on the overlap analysis was also observed for these thresholds. Therefore, it is concluded that the models tested are capable of predicting lighting in the short term in the Amazon region.

The complex network is a method with a high flexibility and easy application. Complex Network allows extracting relevant information from the system, like its organization and dynamics, as well as different indices that allow obtaining particular characteristics. This work studies the communities present on the rain network in the Amazon basin for the austral summer. Summer was used due to the presence of the South American monsoon system (SAMS), since this is the greatest mechanism for modulating precipitation over South America. Once the communities were obtained, the minimum correlation value (MCV) was varied in order to verify the spatial variations of the communities. Where it was verified how certain communities are composed of subcommunities while others simply disappear. Finally, it is shown how the spatial distribution of the subcommunities shows a relationship with the presence of SAMS. However, more detailed analyzes are needed for each of these communities.

Brazil is one of countries highest incidence of lightning in the world and the characterization of thus event can help in the development of public polices and decision-making by authorities to mitigate the socio-economic damage that may be caused. This work presents some analysis of spatio-temporal patterns of lightnings in Brazil in 2020, generated from Self-Organizing Map (SOM) technique. This analysis considers the activity of the lightning in the hourly, daily and monthly periods accumulated in the different Brazilian states. The seasonal variation of lightning was also evaluated, considering the four seasons of 2020. The results showed that the self-organizing maps were efficient in identifying spatio-temporal patterns of lightning, which are highly variability events. Thus, theses results can support the development of new tools or analysis in which the spatio-temporal information lightning is important, for example, in warning and forecasting systems.