We introduce a novel algorithm for the efficient detection and tracking of interesting features in spatial-temporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm is based on the well-known region growing segmentation method. We extended the basic idea towards the analysis of the complete 4-D dataset, identifying segments representing the spatial features and their development over time. Each segment consists of one set of distinct 3-D features per time step. The algorithm keeps track of the successors of each 3-D feature, constructing the so-called event graph of each segment. The precise localization of the splitting events is based on a search for all grid points inside the initial 3-D feature which have a similar distance to all successive 3-D features of the next time step. The merging event is localized analogously considering inverted direction of time. We tested the implementation on a four-dimensional field of wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and computed a climatology of upper-tropospheric jet streams and their events. We compare our results with a previous climatology, investigate the statistical distribution of the merging and splitting events, and illustrate the meteorological significance of the jet splitting events with a case study. A brief outlook is given on additional potential applications of the 4-D data segmentation technique.