Classifying Proximity Soundings with Self-Organizing Maps toward Improving Supercell and Tornado Forecasting

Abstract

he self-organizing map (SOM) statistical technique is applied to vertical profiles of thermodynamic and kinematic parameters from a Rapid Update Cycle-2 (RUC-2) proximity sounding dataset with the goal of better distinguishing and predicting supercell and tornadic environments. An SOM is a topologically ordered mapping of input data onto a two-dimensional array of nodes that can be used to classify large datasets into meaningful clusters. The relative ability of SOMs derived from each parameter to separate soundings in a way that is useful in discriminating between storm type, location, and time of year is discussed. Sensitivity to SOM configuration is also explored. Simple skill scores are computed for each SOM to evaluate the relative potential of each variable for future development as a method of probabilistic forecasting. It is found that variance in SOM nodes is reduced compared to the overall dataset, indicating that this is a viable classification method. SOMs of profiles of wind-derived variables are more effective in discriminating between storm type than thermodynamic variables. The SOM method also identifies meteorological, geographic, and temporal regimes within the dataset. In general, conditional probabilities of storm-type occurrence generated using SOMs have higher skill when wind-derived variables are considered and when forecasting nonsupercell events. Storm-relative wind variables tend to have better skill than ground-relative wind variables when forecasting nonsupercells, whereas ground-relative variables become more important when forecasting tornadoes.