Explosive cyclones occurred between 2010 and 2020 in the South Atlantic under the perspective of two detection schemes.

Under two detection schemes, this study analyzes one of the most destructive weather systems - the explosive cyclones - in the South Atlantic, from 2010 to 2020. Then, two methods are presented to study these systems: the Observational Method (OBSM) and the Automated Method (AUTM). The first uses visual analysis of the mean sea level pressure (mslp) fields and functions to identify the local minimums using the Grid Analysis and Display (GrADS) software. The second utilizes a function from OpenGrADS called mfhilo. It shows the local minimum in the grid using laplacian, magnitude, and percentile. Two shell algorithms for data manipulation are used for the AUTM: one to trace the cyclones' trajectories according to a previously defined fixed area and the other to separate them into explosives. The OBSM methodology showed 271 cases averaging 25 yearly and revealed important characteristics regarding the intensities. According to AUTM's methodology, from the 2705 ordinary cyclone cases identified, 299 are explosives. There is a clear seasonality pattern in the systems' distribution along South America, similar to OBSM, but more highlighted. In summer, they concentrate at high latitudes, while in winter and spring, they are assembled near southern Brazilian and Uruguayan coasts.

Comparing explosive cyclogenesis cases of different intensities occurred in Southern Atlantic.

Six events of explosive cyclogenesis occurred in the south Atlantic were compared using reanalysis data and satellite water vapor imagery. Cases of different intensities (weak, moderate and strong) occurred during 2014 summer season and 2012 winter season were studied. Despite the similarities the tropopause anomaly was more prominent and vertical movements were stronger in the strong cyclogenesis cases. The tropopause anomalies behind the cold front and ahead of the warm front appear only in the mature stage of the weak and moderate cases while in the strong case it is already evident and more intense behind the cold front since the beginning of the cycle. In all the cases confluence of the jet streams took place at higher levels forming a jet streak with difluence occurring downstream and the cyclone beginning in the exit region. The trajectories of the cyclones were in the southeast direction but longer and more meridional in the strong cases. The results indicated the baroclinicity of the region as the main mechanism for the development of these cyclones as well as the amplitude of the upper level jet stream perturbation. Furthermore, all the explosive cyclones developed following the Shapiro & Keyser cyclone conceptual model.

Classification of homogeneous regions of vegetation cover in the State of Rio Grande do Sul, Brazil and its temporal dynamics, using AVHRR GIMMS and MODIS data sets.

This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change.