Finding Storm Track Activity Metrics that are Highly Correlated with Weather Impacts. Part 1: Frameworks for Evaluation and Accumulated Track Activity
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Finding Storm Track Activity Metrics that are Highly Correlated with Weather Impacts. Part 1: Frameworks for Evaluation and Accumulated Track Activity

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  • Journal Title:
    Journal of Climate
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    In the midlatitudes, storm tracks give rise to much of the high-impact weather, including precipitation and strong winds. Numerous metrics have been used to quantify storm track activity, but there has not been any systematic evaluation of how well different metrics relate to weather impacts. In this study, two frameworks have been developed to provide such evaluations. The first framework quantifies the maximum one-point correlation between weather impacts at each grid point and the assessed storm track metric. The second makes use of canonical correlation analysis to find the best correlated patterns and uses these patterns to hindcast weather impacts based on storm track metric anomalies using a leave-N-out cross-validation approach. These two approaches have been applied to assess multiple Eulerian variances and Lagrangian tracking statistics for Europe, using monthly precipitation and a near-surface high-wind index as the assessment criteria. The results indicate that near-surface storm track metrics generally relate more closely to weather impacts than upper-tropospheric metrics. For Eulerian metrics, synoptic time scale eddy kinetic energy at 850 hPa relates strongly to both precipitation and wind impacts. For Lagrangian metrics, a novel metric, the accumulated track activity (ATA), which combines information from both cyclone track frequency and amplitude, is found to be best correlated with weather impacts when spatially filtered 850-hPa vorticity maxima are used to define cyclones. The leading patterns of variability for ATA are presented, demonstrating that this metric exhibits coherent large-scale month-to-month variations that are highly correlated with variations in the mean flow and weather impacts.
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    Journal of Climate, 33(23), 10169-10186. https://journals.ametsoc.org/view/journals/clim/33/23/jcliD200393.x
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