The Brazilian National Institute for Space Research (INPE) produces research that helps to understand climate and weather dynamics in Brazil and in the world, with significant impacts on national public and private strategic planning. Among the essential information for the studies are the rainfall data. In this context, ensuring the quality of this data has a direct impact on the reliability of the forecasts and analysis generated from them. Thus, this study, which is a partnership between INPE, the Laboratory of Atmospheric Physics and Polytechnic School of USP and the ARM-DoE (Atmospheric Radiation Measurement Climate Research Facility), aimed to establish computational tools that could deal with the quality of data from rain in accordance with the main international directives. Thus, it was proposed for this study the development of a specific toolkit for data from the Micro Rain Radar (MRR), disdrometers PARSIVEL2 and RD80, and rain gauge that would help researchers from INPE, USP and partners to: standardize the preparation of raw data for internationally accepted formats; processing figures to support quick analyses; analyze and process data quality and, finally, record metadata and quality analysis for publication in international data repositories.

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.

In this work, machine learning techniques were applied to detect clusters present in satellite and weather radar images. The technique used was the unsupervised clustering algorithm DBSCAN. This algorithm was used to extract the morphological characteristics of atmospheric systems that occurred between February 1 and March 30, 2014 (rainy season) and September 15 to October 15, 2014 (dry season). The morphological characteristics are extracted from different thresholds (235K, 220K and 210K) of cloud top brightness temperature observed in the infrared channel of GOES-13 satellite, and also the precipitation estimated at the reflectivity thresholds (20dBZ, 30dBZ and 40dBZ) of the SIPAM meteorological radar in the city of Manaus. The results present the number of clusters identified by the algorithm and described the characteristics of the clusters during the diurnal cycle and in both seasons.

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.