L. Frank, V. L. Galinsky, L. Orf, and D. Bodine
The mechanisms by which tornadoes are formed within supercell thunderstorms (tornadogenesis) remain a significant scientific mystery. This is a problem of both great scientific interest and social impact, as the ability to understand tornado formation is critical in reducing the time between issuing of tornado warnings to potentially affected populations and tornado formation. However, the interplay of spatio-temporally varying processes within the highly dynamic supercell environment and tornadoes is much too complex to lend itself to analytical modeling, leaving essentially two methods as viable approaches to addressing this problem: numerical simulation and observational methods, such as Doppler radar and in situ observations. But while numerical simulations have the advantage of well-controlled initial conditions and explicitly described dynamics, they are computationally intensive and still can only approximate the physical conditions under which tornadogenesis occurs. Observational methods have their own drawbacks, not the least of which is the difficulty of deploying instruments in advantageous positions in rapidly evolving and hazardous environments and problematic terrain and the difficulty observing critical quantities such as temperature and humidity aloft. Nevertheless, advances in observational technologies, in particular mobile Doppler radar and occasional in-situ observations, have facilitated the acquisition of increasingly accurate spatio-temporal volumetric data in tornadic storms.
As the data integrity and complexity has increased, the role of data analysis has become increasingly critical. However, to date the primary method of analysis in Doppler radar studies of tornadogenesis has been confined to essentially qualitative analyses based on traditional visualization methods such as contour diagrams, isosurfaces, and streamline generation. While these methods are straightforward (though burdensome) to implement, they are limited in their ability to capture and quantify spatio-temporal correlations patterns, or “modes”, that typically characterize such complex systems. This becomes increasingly challenging with large data volumes generated by multi-parameter a (e.g., dual-polarization) and rapid-scan radars, both of which are necessary to completely observe and understand dynamic and microphysical processes.
To address this problem, we recently developed a general approach to the analysis of space-time volumetric data called the entropy field decomposition (EFD) [1,2] and demonstrated the ability to extract spatio-temporal features in both simulated data [3] and actual mobile Doppler radar data hypothesized to be associated with tornadogenesis observed by more traditional, but laborious and primarily qualitative, analysis methods, as well as the ability to reconstruct long-term correlation signatures (or tracts) of the underlying fields (e.g., vorticity), revealing how such long-range coherences are related to estimated local parameters (e.g. vorticity stretching) and detect spatio-temporal modes of measured quantities (e.g., radar reflectivity), providing a quantitative picture of the dynamics of tornadogenesis. The EFD method is both automated and quantitative and provides an analysis approach of increased sensitivity to kinematic features.
Three major limitations from our initial study are addressed in this presentation. First, the spatial and temporal resolution of the numerical simulations has been significantly increased in order to capture critical features of tornado genesis, evolution, and maintenance not observable in our first study. The tornado-scale simulation analyzed is run with 10 m grid spacing with data saved every 0.2s, and contains a violent tornado embedded within a supercell. Second, EFD estimation is now performed on simulated radar data from the numerically generated tornado storm, rather than on the direct output of the numerical simulations, providing data much closer to the observational data. Simulated radar moment data is generated using dual-polarization radar simulators, which account for the scattering of both hydrometeors and debris. Third, the observational data now includes multiple modalities, from high spatial and temporal scales mobile Doppler radars sensitive to tornado-scale processes, to WSR-88D data sensitive to storm-scale processes. A preliminary result is shown in Fig1 where EFD (C) has been applied to simulated radar data (B) derived from the simulated tornadic supercell (A) and automatically detects the tornado within the noisy radar data, and closely resembles the actual simulated tornado. We demonstrate the ability detect spatio-temporal modes of measured quantities (e.g., radar reflectivity), and to reconstruct long-term vorticity correlations and show its relevance to estimated local parameters (vorticity stretching) from multiple spatial and temporal scales, providing a foundation for understanding the complete picture of the dynamics of tornadogenesis, and demonstrating the potential for the EFD method as suitable for use with operational weather radar networks deployed in the service of protecting the public. The EFD computational and visualization software is being integrated into a freely available application for distribution to the severe weather community.
[1] Frank, LR and Galinsky VL. Detecting spatio-temporal modes in multivariate data by entropy field decomposition. J Phys A 2016;49:395001
[2] Frank LR, Galinsky VL, Orf L, Wurman JM. Dynamic multiscale modes of severe storm structure detected in mobile Doppler radar data by entropy field decomposition. J Atmos Sci 2018;75:709–730.
[3] Orf, L., R. Wilhelmson, B. Lee, C. Finley, and A. Houston, 2017: Evolution of a long-track violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc., 98, 45–68, https://doi.org/ 10.1175/BAMS-D-15-00073.1.
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