Density maps are one of the most common and powerful lightning data applications, and they are more efficient the more detailed they are. When working with CG lightning data from lightning location systems, some aspects must be included in the analysis to overcome network performance variations. Two parameters are typically used to evaluate system performance: detection efficiency (DE) and location accuracy (LA).For the Brazilian National Integrated Lightning Detection Network, DE is typically evaluated by models, and LA is analyzed through confidence ellipses. This paper presents climatological analysis of lightning activity, including the most recent relative DE model developed in Brazil, as well as an adapted kernel smoothing method based on confidence ellipses [called Gaussian kernel based on confidence ellipses (GKBCE)] as approaches to minimize and/or include the spatial variation of the system's performance in the analysis. The maps are produced over the central-south portion of Brazil (mainly along ITAIPU power transmission lines), using 11 years of data available from the network (from January 1999 to December 2009). The model increased density by ~20% over the entire region, without making considerable changes to the spatial pattern. The GKBCE seems to work well in smoothing, obtaining better results than the standard cell count (quadrat) method, by working independently of the grid size (allowing the creation of high-resolution maps), and by including location errors in the analysis. The use of these procedures might result in more detailed maps and more suitable results when analyzing lightning data.

Over the decades, hydropower complexes have been built in several hydrographic basins of Brazil including the Amazon region. Therefore, it is important to understand the effects of these constructions on the environment and local communities. This work presents a land use and land cover change temporal analysis considering a 33-year period (1985–2018) in the direct influence zone of the Braço Norte Hydropower Complex, Brazilian Amazonia, using the Collection 4.1 level 3 of the freely available MapBiomas dataset. Additionally, we have assessed the Brazilian Amazon large-scale deforestation process acting as a land use and land cover change driver in the study area. Our findings show that the most impacted land cover was forest formation (from 414 km² to 287 km², a reduction of 69%), which primarily shifted into pasturelands (increase of 664%, from 40 km² to 299 km²). The construction of the hydropower complex also triggered indirect impacts such as the presence of urban areas in 2018 and the consequent increased local demand for crops. Together with the ongoing large-scale Amazonian deforestation process, the construction of the complex has intensified changes in the study area as 56.42% of the pixels were changed between 1985 and 2018. This indicates the importance of accurate economic and environmental impact studies for assessing social and environmental consequences of future construction in this unique region. Our results reveal the need for adopting special policies to minimize the impact of these constructions, for example, the creation of Protected Areas and the definition of locally-adjusted parameters for the ecological-economic zoning considering environmental and social circumstances derived from the local actors that depend on the natural environment to subsist such as indigenous peoples, riverine population, and artisanal fishermen.

Eleven years of lightning data from the Brazilian Integrated National Lightning Detection Network were used to analyze the effects of the urban heat island (UHI) of Sao Paulo on lightning activity, extending the investigation of previous works. Cloud‐to‐ground lightning data were analyzed in both spatial and temporal perspectives, using different approaches: flash density, flash rate, thunderstorm hours (TH), and the cell initiation technique (CIT), which aims to identify the onset of thunderstorms. Land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer) was used to analyze the UHI evolution over the years. MODIS data were validated using ground stations, distributed within the urban area. Different time intervals (seasonal and intraday) were used in an attempt to separate local convective systems from synoptic‐scale events. The results indicate significant effects of the UHI (using LST) on THs and CIT. The CIT showed a nearly ring pattern, especially during the afternoon (14:00–18:00 LT) of summer months, reinforcing temperature contrast as a condition for storm initiation. The results also suggest an amplification of the UHI effects on thunderstorm activity by local factors (sea and country breeze, synoptic events, and terrain). Higher flash rates were also observed throughout the urban region, which influences the lightning density. Temporal analysis indicates that minimum temperature and lightning activity increase in wintertime. In summary, the results agree with previous studies about the UHI and indicate its importance on lightning occurrence, especially by increasing the temperature contrast and the instability in these regions.

One of the most interesting attributes of Lightning Location Systems (LLSs) data is that they can be analyzed in several ways according to the objectives of the study. However, the quality of the data is governed by the system performance and has some limitations when analyzed at different temporal/spatial scales, and these limitations will depend on the analysis method. This work focuses on approaches to minimize the variations associated with LLS performance. In this way, specific network configurations for the Brazilian Lightning Detection Network (BLDN) were obtained through the reprocessing of selected sensor data, resulting in three distinct datasets. Each dataset was then evaluated using different procedures: trimmed flash (exclusion of low current discharges), thunderstorm days (TDs), and thunderstorm hours (THs). The comparison between TDs obtained from the LLS and TDs available from surface stations shows consistent results with a good correlation of those datasets. An 11-yr analysis of BLDN data also shows that improvement (over time) of the system sensitivity has led to the detection of an increasing number of low peak current events. By eliminating low peak current discharges (less than 19 kA), the sensitivity variation was significantly reduced, partially “normalizing” long-term performance. TDs and THs were the most effective method to normalize temporal variations of the lightning activity, overcoming most of the network performance variations. From the spatial perspective, TDs and THs also seem to produce the most reliable lightning distribution. These results might guide long-term temporal and spatial analysis of lightning data, providing a more stable approach that is independent of system performance.