Ensemble analysis of spatiotemporal attributes derived from objectively identified three-dimensional atmospheric fronts

Ensemble simulations have become a standard in numerical weather prediction (NWP). However, ensemble simulations generate large amounts of data, and their comprehensive analysis remains challenging. We introduce a feature-based NWP ensemble analysis method based on the well-established conceptual model of atmospheric fronts. Recent developments in front detection techniques have enabled a reliable, robust, and objective identification of three-dimensional (3-D) frontal structures in NWP data.

We advance detection of individual front features towards front-feature-based time series analysis and ensemble clustering. We track 3-D fronts of a selected cyclone system to create time series of frontal attributes. These frontal attributes characterize properties of the tracked front, for example, the maximum strength of the temperature gradient across the frontal zone, the average slope of the 3-D frontal structure or associated upward motion. The obtained time series provide a compact overview of the development of front characteristics. For ensemble analysis, we generate such time series for the cyclone system as represented in the different ensemble members. Time series distance measures including dynamic time warping can then be utilized to analyze similarities and differences of frontal development in the ensemble, for example, for front-feature-based ensemble cluster analysis. Also, a time window similarity search enables users to select a specific event of interest (for example, a sudden increase in frontal strength) in one of the ensemble members and to search for similar events in other members.

Integrated in the 3-D visual analysis framework Met.3D, our approach facilitates a comprehensive analysis of the spatiotemporal development of 3-D atmospheric fronts and thus contributes to the challenge of rapidly analyzing large ensemble weather predictions.