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1136 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 25 Tornado Detection Using a Neuro–Fuzzy System to Integrate Shear and Spectral Signatures YADONG WANG,TIAN-YOU YU, AND MARK YEARY School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma ALAN SHAPIRO School of Meteorology, University of Oklahoma, Norman, Oklahoma SHAMIM NEMATI Department of Mathematics, University of Oklahoma, Norman, Oklahoma MICHAEL FOSTER AND DAVID L. ANDRA JR. National Weather Service, Norman, Oklahoma MICHAEL JAIN National Severe Storms Laboratory, Norman, Oklahoma (Manuscript received 25 April 2007, in final form 22 October 2007) ABSTRACT Tornado vortices observed from Doppler radars are often associated with strong azimuthal shear and Doppler spectra that are wide and flattened. The current operational tornado detection algorithm (TDA) primarily searches for shear signatures that are larger than the predefined thresholds. In this work, a tornado detection procedure based on a fuzzy logic system is developed to integrate tornadic signatures in both the velocity and spectral domains. A novel feature of the system is that it is further enhanced by a neural network to refine the membership functions through a feedback training process. The hybrid ap- proach herein, termed the neuro–fuzzy tornado detection algorithm (NFTDA), is initially verified using simulations and is subsequently tested on real data. The results demonstrate that NFTDA can detect tornadoes even when the shear signatures are degraded significantly so that they would create difficulties for typical vortex detection schemes. The performance of the NFTDA is assessed with level I time series data collected by the KOUN radar, a research Weather Surveillance Radar-1988 Doppler (WSR-88D) operated by the National Severe Storms Laboratory (NSSL), during two tornado outbreaks in central Oklahoma on 8 and 10 May 2003. In these cases, NFTDA and TDA provide good detections up to a range of 43 km. Moreover, NFTDA extends the detection range out to approximately 55 km, as the results indicate here, to detect a tornado of F0 magnitude on 10 May 2003. 1. Introduction later was suggested as an indicator of tornadoes (Fujita 1958). However, Forbes (1981) found that more than The subjective detection of potentially tornadic half of the tornadoes in his study did not exhibit appar- storms using hook-shaped returns in a radar’s display ent hook signatures and suggested that hook echoes was first documented by Stout and Huff (1953), and may not be a reliable indicator. A unique feature of strong azimuthal velocity difference at a constant range, termed the tornado vortex signature (TVS), was Corresponding author address: Yadong Wang, 202 W. Boyd, School of Electrical and Computer Engineering, University of first observed by Burgess et al. (1975) and Brown et al. Oklahoma, Norman, OK 73019. (1978) using a pulsed Doppler radar. The national net- E-mail: [email protected] work of Weather Surveillance Radar-1988 Doppler DOI: 10.1175/2007JTECHA1022.1 © 2008 American Meteorological Society Unauthenticated | Downloaded 09/30/21 01:56 PM UTC JTECHA1022 JULY 2008 WANGETAL. 1137 (WSR-88D) has been proven to improve the probabil- wide and flat features of the spectrum (Yeary et al. ity of detection (POD) and the warning lead time for 2007). tornadoes in the United States (Polger et al. 1994; Although each tornadic signature described above Bieringer and Ray 1996; Simmons and Sutter 2005). has the potential to facilitate tornado detection to some The basic idea of the current tornado detection algo- extent, it is possible to optimally integrate all of the rithm (TDA) is to search for strong and localized azi- available signatures to improve the detection based on muthal shear in the field of mean radial velocities (e.g., a single signature. A fuzzy logic methodology is ideal Crum and Alberty 1993; Mitchell et al. 1998). However, for addressing a complicated system that launches a because of the smoothing effect caused by the radar decision based on multiple inputs simultaneously. resolution volume, the shear signature can be signifi- Fuzzy logic–based systems have already been widely cantly degraded if the size of tornado is small and/or the applied to weather radar for hydrometeor classification tornado is located at far ranges (Brown and Lemon (e.g., Vivekanandan et al. 1999; Liu and Chandrasekar 1976). Recently, Brown et al. (2002) demonstrated that 2000; Zrnic´ et al. 2001). In this work, a fuzzy logic sys- the shear signature can be enhanced using half-degree tem is developed to integrate tornadic signatures in angular sampling despite the expense of slightly in- both the spectral and velocity domains. The system is creasing statistical errors in velocity data. Better tor- further enhanced by a feedback process provided nado signatures can be observed by mobile radars be- through a neural network and is termed the neuro– cause of their enhanced resolution in both temporal fuzzy tornado detection algorithm (NFTDA). and spatial domains (e.g., Bluestein et al. 2003; Wur- This paper is organized as follows. An overview of man and Alexander 2006; Bluestein et al. 2007b). A the characterization of tornado signatures is presented conical debris envelope, a low-reflectivity eye, and mul- in section 2. The NFTDA technique is developed in tiple semiconcentric bands of reflectivity surrounding section 3 and is followed by the simulation results in the eye have been observed using Doppler on Wheels section 4. The performance of NFTDA is further dem- (DOW) radar (Wurman and Gill 2000; Burgess et al. onstrated and evaluated using time series data collected 2002). In addition, Ryzhkov et al. (2005) have shown by the research WSR-88D (KOUN), operated by the that significant debris signatures can be observed in National Severe Storms Laboratory (NSSL), and com- tornadoes using an S-band polarimetric radar. Simi- pared to the operational TDA in section 5. Finally, a larly, anomalously low values of differential reflectivity summary and conclusions are given in section 6. ␳ ZDR, low cross-correlation coefficient h␷, and high- Z reflectivity were also observed by a mobile, dual- 2. An overview of tornado signature polarization X-band Doppler radar (Bluestein et al. characterization 2007a). Zrnic´ and Doviak (1975) have shown that tornado The TVS, which is exemplified by extreme values of spectra can have wide and bimodal signatures that set radial velocities with opposite signs over a small azi- them apart from other weather spectra. These distinct muthal distance, has been widely used as an indicator tornado spectral signatures (TSS) were subsequently for tornadoes (e.g., Burgess et al. 1975; Brown 1998; verified by a pulsed Doppler radar with a significant Brown et al. 2002). In the NSSL’s TDA, the velocity maximum unambiguous velocity of approximately 90 differences between adjacent gates are grouped to form msϪ1 (Zrnic´ et al. 1977; Zrnic´ and Istok 1980; Zrnic´ et a 3D feature based on multiple thresholds to facilitate al. 1985). Recent studies have shown that spectra simi- tornado detection (Mitchell et al. 1998). Moreover, TSS lar to white noise, but with significant signal power, with bimodal or white-noise-like features have been ob- can be observed in a tornadic region using numerical served from both real data and simulations (e.g., Zrnic´ simulations and data collected from WSR-88D with and Doviak 1975; Zrnic´ et al. 1985; Yu et al. 2007). It is operational setups (Yu et al. 2007). In that study, three noted that the Doppler spectrum represents a distribu- complementary parameters were introduced to quan- tion of weighted radial velocities within the radar reso- tify TSS, and these features were derived from high- lution volume, and the mean Doppler velocity is de- order spectral analysis and signal statistics. It was fined by their statistical average (i.e., the first moment). shown that the TSS still can be significant enough to It has been hypothesized in Yu et al. (2007) that the facilitate tornado detection at far ranges, even though TSS can retain enough information to facilitate tornado the shear signature may become difficult to identify. detection, while the TVS is degraded by the smoothing Moreover, the eigenvalues of the correlation matrix de- effect and becomes difficult to identify. Three feature rived from the raw time series data also have a distinct parameters were proposed by Yu et al. (2007) to char- distribution in the tornadic region resulting from the acterize the TSS. The first parameter is the spectrum Unauthenticated | Downloaded 09/30/21 01:56 PM UTC 1138 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 25 width (␴␷), which is also known as the second spectral radar continuously over the entire tornadic event for moment. Although the spectrum width is an intuitive approximately1hon10May, but there were only two parameter used to describe the wide spectral feature, it volume scans of data on 8 May. The data from the is not sufficient to characterize the shape of a tornadic lowest two elevation angles (0.5° and 1.5°) were used to spectrum and is susceptible to a number of factors, such calculate the histograms, which were normalized by the as inaccurate estimate of noise level and radar settings total number of data used in the analysis. A tornadic (Fang et al. 2004). Moreover, large spectrum widths can case is defined by the gate where the velocity difference be observed in a nontornadic region where strong lin- of its adjacent azimuthal gates is larger than 20 m sϪ1 ear shear and/or low signal-to-noise ratio (SNR) are and within the tornado damage path. Regions outside present. Two additional feature parameters—the phase the damage path with an SNR larger than 20 dB are of the radially integrated bispectrum (PRIB; denoted defined as nontornadic cases. It is shown that the tor- ␴ ␴ ⌬ ␹ by P) and spectrum flatness s—were introduced to nadic cases are associated with large ␷, V, P, and R, ␴ characterize TSS in Yu et al.

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