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A Novel Approach of Tornado Detection Using a Machine Intelligence

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A Novel Approach of Tornado Detection Using a Machine Intelligence P5.17 1 A NOVEL APPROACH OF TORNADO DETECTION USING A MACHINE INTELLIGENCE SYSTEM BASED ON SHEAR AND SPECTRAL SIGNATURES ¢¡¤£ ¥ ¦ § Yadong Wang , Tian-You Yu , Mark Yeary , Alan Shapiro , Shamim Nemati , Michael Foster , § ¨ David L. Andra Jr. , Michael Jain School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA ¥ School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA ¦ Department of Mathematics, University of Oklahoma, Norman, Oklahoma, USA § National Weather Service, Norman, Oklahoma, USA ¨ National Severe Storms Laboratory, Norman, Oklahoma, USA 1. INTRODUCTION with a significant maximum unambiguous velocity of ap- h proximately 90 m s g [Zrnic´ et al., 1977; Zrnic´ and Is- The subjective detection of potentially tornadic storms tok, 1980; Zrnic´ et al., 1985]. Recent studies have using hook shape returns in a radar’s display was first shown that wide and flattened spectra are observed documented by Stout and Huff [1953], and was sug- in a tornadic region using simulations and data col- gested as an indicator of tornadoes after the Illinois tor- lected from the research WSR-88D (KOUN) operated nado [Fujita, 1958]. However, Forbes [1981] found that by the National Severe Storms Laboratory (NSSL) [Yu more than half of the tornadoes in his study did not ex- et al., 2007]. In that study, three complementary feature hibit apparent hook signatures and suggested that hook parameters that were derived from high-order spectral echoes may not be a reliable indicator. A unique fea- analysis and signal statistics were introduced to quantify ture of a strong azimuthal velocity difference at a con- TSS. It was suggested that the TSS can still be signifi- stant range, termed tornado vortex signature (TVS), was cant enough to facilitate tornado detection at far ranges, first observed by [Burgess et al., 1975; Brown et al., even though the shear signature may become difficult 1978] using a pulsed Doppler radar. It has been shown to identify. Additionally, the eigen-ratio of the correlation that the probability of detection (POD), and the warning matrix derived from the raw time series data also have a lead time for tornadoes in the United States were im- distinct distribution in the tornadic region due to the wide proved after the installation of the national network of and flat features of the spectrum [Yeary et al., 2007]. Weather Surveillance Radar-1988 Doppler (WSR-88D) Although the tornadic signatures described above have radars [Polger et al., 1994; Bieringer and Ray, 1996; the potential to facilitate tornado detection, each of Simmons and Sutter, 2005]. The basic idea of the cur- these signatures has different characteristics and it is rent tornado detection algorithm (TDA) is to search for desirable to integrate them to improve the detection. strong and localized azimuthal shear in the field of mean A fuzzy logic methodology is ideal to address a com- radial velocities [e.g., Crum and Alberty, 1993; Mitchell plicated system which launches a decision based on et al., 1998]. However, because of the smoothing effect multiple inputs simultaneously. Fuzzy logic based sys- of the radar resolution volume, the shear signature is de- tems have already been widely applied to weather radar graded if the size of tornado is small and/or the tornado for hydrometeor classification [e.g., Vivekanandan et al., is located at far ranges [Brown and Lemon, 1976]. Re- 1999; Liu and Chandrasekar, 2000; Zrnic´ et al., 2001]. cently, Brown et al. [2002] demonstrated that shear sig- In this work, a fuzzy logic system is developed to inte- nature can be enhanced using half-degree angular sam- grate tornadic signatures in both the spectral and veloc- pling despite the expense of increasing statistical errors ity domains. The system is further enhanced by a feed- in velocity data. back process provided through a neural network and The pioneering work of Zrnic´ and Doviak [1975] has is termed the neuro-fuzzy tornado detection algorithm shown that tornado spectra can have distinct signa- (NFTDA). tures that set them apart from other weather spectra. This paper is organized as follows. The overview of TSS The wide and bimodal tornado spectral signature (TSS) and NFTDA technique is developed in section 2 and is were subsequently verified by a pulsed Doppler radar followed by the simulation results in section 3. The per- 9:(;&<&8 =?> © ¢ ¤! #"! $% &('*),+.-!/021435+.021267$028 formance of NFTDA is further demonstrated using time /.@BA4C%DE+.F2/GH+!6IJ&F2//D!/.@K*DL:(J=&;M8LJ+.D!+.02-NO/GQP!R!=&:(;SK*0!18L02:(:T;M8L02126 series data collected by the KOUN radar in section 4. Fi- 7U0!8 9:T;M<&8 =?>V/.@4A4CDE+.F!/GH+!6SWU/;MGH+.0X6¢A4C%DE+.F2/GH+ZY.[\!]T^!_`:(aGH+.8LDb :(-2R >!-ed4/RXf nally, a summary and conclusions are given in section 5. c P5.17 2 Figure 1: A schematic diagram of NFTDA. A fuzzy logic system is designed to detect a tornado, while a neural network is incorporated to refine the membership functions through a hybrid self-learning process. 2. NEURO-FUZZY TORNADO DETECTION ALGO- additional feature parameters, the phase of the radially RITHM (NFTDA) integrated bispectrum (PRIB, denoted by £ ) and spec- trum flatness ¢¤ , were introduced to characterize TSS [Yu et al., 2007]. Since most shape information of a pat- 2.1. An Overview of Tornado Spectral Signatures tern could be contained in the phase of its Fourier coef- ficients [Oppenheim and Lim, 1981] and the commonly TSS with bimodal or white noise like features have been used power spectrum (the second order spectrum) is observed from both real data and analytical simulations phase blind. A third order spectrum termed “bispectrum” [e.g., Zrnic´ and Doviak, 1975; Zrnic´ et al., 1985; Yu was introduced to extract the phase information by con- et al., 2007]. It is noted that the Doppler spectrum rep- sidering the Doppler spectrum in units of decibel (dB) as resents a distribution of weighted radial velocities within a sequence for pattern recognition [Yu et al., 2007]. The the radar resolution volume, and the mean Doppler ve- spectrum flatness, defined as the variance of a Doppler locity is defined by its statistical average (i.e., the first spectrum in dB, can be used to identify a white-noise like moment). It has been hypothesized [Yu et al., 2007] that feature, which is often observed if the maximum unam- the TSS can retain enough information to facilitate tor- biguous velocity is smaller than the maximum rotational nado detection, while the TVS is smoothed within the speed of a tornado’s vortex. In the cases considered £ radar resolution volume and becomes difficult to iden- [Yu et al., 2007], significantly high and low ¥¤ values tify. Three feature parameters were proposed by Yu were obtained from spectra in a tornado compared to et al. [2007] to characterize the TSS. The first param- spectra from non-tornadic regions. Furthermore, Yeary et al. [2007] reported that a spectrum of white-noise like eter is spectrum width ( ¢¡ ), the second moment of a spectrum. Although the spectrum width is an intuitive signature can reflect on the distribution of eigen-ratio of parameter to describe the wide signature, it is not suf- the correlation matrix estimated from the raw time se- ficient to characterize the shape of a tornadic spectrum ries data. It is found that the regions of a large eigen- and is susceptible to a number of factors such as inac- ratio ( ¦¨§ ) are well correlated with wide and flat spectra curate estimate of noise level and radar settings [Fang in tornadic regions. et al., 2004]. Moreover, large spectrum widths can be In this work, a fuzzy logic system is developed to in- observed in a non-tornadic region where strong shear tegrate tornadic signatures, which includes the velocity and/or low signal to noise ratio (SNR) are present. Two P5.17 3 difference, spectrum width, spectral flatness, PRIB, and using a neural network as depicted in Fig. 1 [Liu and eigen-ratio. Chandrasekar, 2000; Wang et al., 2005]. 2.2. Architecture of Neuro-Fuzzy Tornado Detec- 3. SIMULATION RESULTS tion Algorithm The NFTDA is tested and verified using the Level I time A fuzzy logic system can be considered as a non-linear series data generated from a radar simulator developed mapping of feature parameters (i.e., inputs) to a binary by Yu et al. [2007]. A model Doppler spectrum is sim- output. In NFTDA the output is a binary detection of ulated based on the superposition of weighted scat- the presence of tornado. A typical fuzzy logic system terers’ velocities in the radar resolution volume. The can consist of three subsystems: fuzzification, rule infer- weights are determined by the reflectivity, antenna pat- ence, and defuzzification [Mendel, 1995]. A schematic tern, and range weighting function. If the radial veloc- diagram of the NFTDA is depicted in Fig. 1. In fuzzifi- ity of a scatterer exceeds the maximum unambiguous cation, each feature parameter (or termed crisp input) velocity ( ), it is aliased into the interval of . is converted to a fuzzy variable with a value between Consequently, the time series data are obtained from ¢¡¤£¦¥¨§ , termed membership degree, by a membership the inverse Fourier transform of the model spectrum function for each class. The fuzzy variables are the in- with desirable SNR. A detailed description of the sim- puts to the subsystem of rule inference with an output ulation is provided in Yu et al. [2007]. In this work, a © of © and for tornadic and non-tornadic cases, re- tornado located at 1 km southwest of a mesocyclone spectively, as shown in Fig. 1. The relationship between is simulated. Both a tornado and mesocyclone are the input and output of rule inference is described by modeled by a Rankine combined vortex model with a h h g fuzzy rules.
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