Thunderstorm mode strongly impacts the likelihood and predictability of tornadoes and other hazards, and thus is of great interest to severe weather forecasters and researchers. It is often impossible for a forecaster to manually classify all the storms within convection-allowing model (CAM) output during a severe weather outbreak, or for a scientist to manually classify all storms in a large CAM or radar dataset in a timely manner. Automated storm classification techniques facilitate these tasks and provide objective inputs to operational tools, including machine learning models for predicting thunderstorm hazards. Accurate storm classification, however, requires accurate storm segmentation. Many storm segmentation techniques fail to distinguish between clustered storms, thereby missing intense cells, or to identify cells embedded within quasi-linear convective systems that can produce tornadoes and damaging winds. Therefore, we have developed an iterative technique that identifies these constituent storms in addition to traditionally identified storms. Identified storms are classified according to a seven-mode scheme designed for severe weather operations and research. The classification model is a hand-developed decision tree that operates on storm properties computed from composite reflectivity and mid-level rotation fields. These properties include geometrical attributes, whether the storm contains smaller storms or resides within a larger-scale complex, and whether strong rotation exists near the storm centroid. We evaluate the classification algorithm using expert labels of 400 storms simulated by the NSSL Warn-on-Forecast System or analyzed by the NSSL Multi-Radar / Multi-Sensor product suite. The classification algorithm emulates expert opinion reasonably well (e.g., 76% accuracy for supercells), and therefore could facilitate a wide range of operational and research applications.
A multi-radar analysis of the 20 May 2013 Moore, Oklahoma, U.S. supercell is presented using three Weather Surveillance Radars 1988 Doppler (WSR-88Ds) and PX-1000, a rapid-scan, polarimetric, X-band radar, with a focus on the period between 1930 and 2008 UTC, encompassing supercell maturation through rapid tornado intensification. Owing to the 20-s temporal resolution of PX-1000, a detailed radar analysis of the hook echo is performed on (1) the microphysical characteristics through a hydrometeor classification algorithm (HCA)—inter-compared between X- and S-band for performance evaluation—including a hail and debris class and (2) kinematic properties of the low-level mesocyclone (LLM) assessed through ΔVr analyses. Four transient intensifications in ΔVr prior to tornadogenesis are documented and found to be associated with two prevalent internal rear-flank downdraft (RFD) momentum surges, the latter surge coincident with tornadogenesis. The momentum surges are marked by a rapidly advancing reflectivity (ZH) gradient traversing around the LLM, descending reflectivity cores (DRCs), a drop in differential reflectivity (ZDR) due to the advection of smaller drops into the hook echo, a decrease in correlation coefficient (ρhv), and the detection of debris from the HCA. Additionally, volumetric analyses of ZDR and specific differential phase (KDP) signatures show general diffusivity of the ZDR arc even after tornadogenesis in contrast with explosive deepening of the KDP foot downshear of the updraft. Similarly, while the vertical extent of the ZDR and KDP columns decrease leading up to tornadogenesis, the phasing of these signatures are offset after tornadogenesis, with the ZDR column deepening the lagging of KDP.
While storm identification and tracking algorithms are used both operationally and in research, there exists no single standard technique to objectively determine performance of such algorithms. Thus, a comparative skill score is developed herein which consists of four parameters, three of which constitute the quantification of storm attributes – size consistency, linearity of tracks, and mean track duration – and the fourth which correlates performance to an optimal post-event reanalysis. The skill score is a cumulative sum of each of the parameters normalized from zero to one amongst the compared algorithms, such that a maximum skill score of four can be obtained. The skill score is intended to favor algorithms which are efficient at severe storm detection, i.e., high-scoring algorithms should detect storms that have higher current or future severe threat and minimize detection of weaker, short-lived storms with low severe potential. The skill score is shown to be capable of successfully ranking a large number of algorithms, both between varying settings within the same base algorithm and between distinct base algorithms. Through a comparison with manually-created user datasets, high-scoring algorithms are verified to match well with hand analyses, demonstrating appropriate calibration of skill score parameters.
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