An objective four-dimensional (4D) algorithm developed to track extratropical relative vorticity anomaly 3D structure over time is introduced and validated. The STACKER algorithm, structured with the TRACKER single-level tracking algorithm as source of the single-level raw tracks, objectively combines tracks from various levels to determine the 3D structure of the cyclone (or anticyclone) events throughout their life cycle. STACKER works progressively, beginning with two initial levels and then adding additional levels to the stack in a bottom-up and/or top-down approach. This allows an iterative stacking approach, adding one level at a time, resulting in an optimized 4D determination of relative vorticity anomaly events. A two-stage validation process is carried out with the ECMWF reanalysis ERA-Interim dataset for the 2015 austral winter. First the overall tracking capability during an austral winter, taking into account a set of climate indicators and their impacts on Southern Hemisphere circulation, was compared to previous climatologies, in order to verify the density and distribution of the cyclone events detected by STACKER. Results show the cyclone density distribution is in very good agreement with previous climatologies, after taking into account potential differences due to climate variability and different tracking methodologies. The second stage focuses on three different long-lived events over the Southern Hemisphere during the winter of 2015, spanning seven different pressure levels. Both GOES satellite imagery, infrared and water vapour channels, and ERA-Interim cloud cover products are used in order to validate the tracks obtained as well as the algorithm's capability and reliability. The observed 3D cyclone structures and their time evolution are consistent with current understanding of cyclone system development. Thus, the two-stage validation confirms that the algorithm is suitable to track multilevel events, and can follow and analyse their 3D life cycle and develop full 3D climatologies and climate variability studies.

An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.