Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: Mark Yeary x
  • All content x
Clear All Modify Search
Tian-You Yu, Yadong Wang, Alan Shapiro, Mark B. Yeary, Dusan S. Zrnić, and Richard J. Doviak

Abstract

Distinct tornado spectral signatures (TSSs), which are similar to white noise spectra or have bimodal features, have been observed in both simulations and real data from Doppler radars. The shape of the tornado spectrum depends on several parameters such as the range of the tornado, wind field within the storm, and the reflectivity structure. In this work, one of the higher-order spectra (HOS), termed bispectrum, is implemented to characterize TSS, in which the Doppler spectrum is considered a 1D pattern. Bispectrum has been successfully applied to pattern recognition in other fields owing to the fact that bispectrum can retain the shape information of the signal. Another parameter, termed spectral flatness, is proposed to quantify the spectrum variations. It is shown in simulation that both parameters can characterize TSS and provide information in addition to the three spectral moments. The performance of the two parameters and the spectrum width for characterizing TSS are statistically analyzed and compared for various conditions. The potential of the three parameters for improving tornado detection is further demonstrated by tornadic time series data collected by a research Weather Surveillance Radar-1988 Doppler, KOUN, operated by the National Severe Storms Laboratory.

Full access
Yadong Wang, Tian-You Yu, Mark Yeary, Alan Shapiro, Shamim Nemati, Michael Foster, David L. Andra Jr., and Michael Jain

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 approach 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.

Full access
Robert Palmer, Mark Yeary, Michael Biggerstaff, Phillip Chilson, Jerry Crain, Kelvin Droegemeier, Yang Hong, Alexander Ryzhkov, Terry Schuur, Sebastián Torres, Tian-You Yu, Guifu Zhang, and Yan Zhang
Full access
Bradley Isom, Robert Palmer, Redmond Kelley, John Meier, David Bodine, Mark Yeary, Boon-Leng Cheong, Yan Zhang, Tian-You Yu, and Michael I. Biggerstaff

Abstract

Mobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamforming techniques. Adaptive algorithms allow for improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during two severe weather events in Oklahoma. Several digital beamforming methods were tested and analyzed, producing unique, simultaneous multibeam measurements using the AIR.

Full access
Mark Weber, Kurt Hondl, Nusrat Yussouf, Youngsun Jung, Derek Stratman, Bryan Putnam, Xuguang Wang, Terry Schuur, Charles Kuster, Yixin Wen, Juanzhen Sun, Jeff Keeler, Zhuming Ying, John Cho, James Kurdzo, Sebastian Torres, Chris Curtis, David Schvartzman, Jami Boettcher, Feng Nai, Henry Thomas, Dusan Zrnić, Igor Ivić, Djordje Mirković, Caleb Fulton, Jorge Salazar, Guifu Zhang, Robert Palmer, Mark Yeary, Kevin Cooley, Michael Istok, and Mark Vincent

Abstract

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

Full access