Remote Turbulence Detection Using Ground-Based Doppler Weather Radar (2024)

1. Al-Momar, S., Deierling, W., Williams, J.K.: Examining in-cloud convective turbulence in relation to total lightning and the 3D wind field of severe thunderstorms. In: AMS 17th Conference on Aviation, Range, and Aerospace Meteorology, Paper 5.3 (2015)
2. Bedka, K.M., Brunner, J., Dworak, R., Feltz, W., Otkin, J., Greenwald, T.: Objective satellite-based overshooting top detection using infrared window channel brightness temperature gradients. J. Appl. Meteor. Climatol. 49, 181–202 (2010)
   https://doi.org/10.1175/2009JAMC2286.1
3. Cornman, L.B., Carmichael, B.: Varied research efforts are underway to find means of avoiding air turbulence. ICAO J. 48, 10–15 (1993)
4. Cornman, L.B., Morse, C.S., Cunning, G.: Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements. J. Aircraft 32, 171–177 (1995)
   https://doi.org/10.2514/3.46697
5. Cornman, L.B., Goodrich, R.K.: The detection of atmospheric turbulence using Doppler radars. Preprints, Workshop on Wind Shear and Wind Shear Alert Systems. Oklahoma City, 13-15 November. Am. Meteor. Soc., Boston (1996)
6. Cornman, L.B., Williams, J., Meymaris, G., Chorbajian, B.: Verification of an airborne radar turbulence detection algorithm. In: 6th International Symposium on Tropospheric Profiling: Needs and Technologies, 9–12 (2003)
7. Craig, J.A., Williams, J.K., Blackburn, G., Linden, S., Stone, R.: Remote detection and real-time alerting for in-cloud turbulence. In: AMS 13th Conference on Aviation, Range and Aerospace Meteorology, Paper 9.4 (2008)
8. Deierling, W., Williams, J.K.: The relationship of in-cloud convective turbulence to total lightning. In: AMS 15th Conference on Aviation, Range, and Aerospace Meteorology, Paper 2.3 (2011)
9. Doviak, R.J., Zrnić, D.S.: Doppler Radar and Weather Observations. Academic, San Diego, CA (1993)
10. Fang, M., Doviak, R.J.: Coupled contributions in the Doppler radar SW equation. J. Atmos. Oceanic Technol. 25, 2245–2258 (2008)
    https://doi.org/10.1175/2007JTECHA968.1
11. Fang, M., Doviak, R.J., Melnikov, V.: SW Measured by WSR-88D: Error Sources and Statistics of Various Weather Phenomena. J. Atmos. Oceanic Technol. 21, 888–904 (2004)
    https://doi.org/10.1175/1520-0426(2004)021<0888:SWMBWE>2.0.CO;2
12. Frehlich, R.G., Yadlowsky, M.J.: Performance of mean-frequency estimators for Doppler radar and lidar. J. Atmos. Oceanic Technol. 11, 1217–1230; corrigenda, 12, 445–446 (1994)
13. Hubbert, J.C., Dixon, M., Ellis, S.M.: Weather radar ground clutter. Part II: Real-time identification and filtering. J. Atmos. Oceanic Technol. 26, 1181–1197 (2009)
    https://doi.org/10.1175/2009JTECHA1160.1
14. Istok, M.J., Doviak, R.J.: Analysis of the relation between Doppler spectral width and thunderstorm turbulence. J. Atmos. Sci. 43, 2199–2214 (1986)
    https://doi.org/10.1175/1520-0469(1986)043<2199:AOTRBD>2.0.CO;2
15. Kaplan, M.L., Huffman, A.W., Lux, K.M., Charney, J., Riordan, A.J., Lin, Y.-L.: Characterizing the severe turbulence environments associated with commercial aviation accidents. Part 1: a 44-case study synoptic observational analysis. Meteor. Atmos. Phys. 88, 129–153 (2005)
    https://doi.org/10.1007/s00703-004-0080-0
16. Kessinger, C., Ellis, S., Van Andel, J.: The radar echo classifier: a fuzzy logic algorithm for the WSR-88D. In: 3rd AMS Conference on Artificial Intelligence Applications to Environmental Science, Long Beach, 9–13 Feb (2003)
17. Lane, T.P., Sharman, R.D., Trier, S.B., Fovell, R.G., Williams, J.K.: Recent advances in the understanding of near-cloud turbulence. Bull. Am. Meteor. Soc. 93, 499–515 (2012)
    https://doi.org/10.1175/BAMS-D-11-00062.1
18. Lindholm, T.A., Frazier, E., Barron, B., Blackburn, G., Kessinger, C., Delemarre, M., Williams, J.K.: Demonstrating feasibility of tactical turbulence alerts. In: AMS 17th Conference on Aviation, Range and Aerospace Meteorology, Paper 13.3 (2015)
19. Melnikov, V.M., Doviak, R.J.: Turbulence and wind shear in layers of large Doppler SW in stratiform precipitation. J. Atmos. Oceanic Technol. 26, 430–443 (2009)
    https://doi.org/10.1175/2008JTECHA1108.1
20. Melnikov, V.M., Zrniç, D.S.: Estimates of large SW from autocovariances. J. Atmos. Oceanic Technol. 21, 969–974 (2004)
    https://doi.org/10.1175/1520-0426(2004)021<0969:EOLSWF>2.0.CO;2
21. Meymaris, G., Williams, J., Hubbert, J.: An improved hybrid SW estimator. In: 34th AMS Conference on Radar Meteorology, Paper P5.20 (2009)
22. Meymaris, G., Williams, J., Hubbert, J.: Hybrid SW estimator. NCAR Report submitted to the NEXRAD Radar Operational Center, 6 pp (2011)
23. Sharman, R., Frehlich, R.: Aircraft scale turbulence isotropy derived from measurements and simulations. In: Proc. AIAA 41st Aerospace Science Meeting and Exhibit, Paper 2003-194 (2003)
    https://doi.org/10.2514/6.2003-194
24. Sharman, R.D., Cornman, L.B., Meymaris, G., Pearson, J., Farrar, T.: Description and derived climatologies of automated in situ eddy-dissipation-rate reports of atmospheric turbulence. J. Appl. Meteor. Climatol. 53, 1416–1432 (2014)
    https://doi.org/10.1175/JAMC-D-13-0329.1
25. Sirmans, D.R., Gunther, R., Windes, J.: Engineering study of spectrum width anomaly. Informal Report submitted to the Operational Support Facility of the National Weather Service, Norman, OK, 10 pp (1997)
26. Williams, J.K.: “Introduction to Fuzzy Logic” (Chapter 6). In: Haupt, S.E., Marzban, C., Pasini, A. (eds.) Artificial Intelligence Methods in the Environmental Sciences, 424 pp. Springer, New York (2009)
27. Williams, J.K.: Using random forests to diagnose aviation turbulence. Mach. Learn. 95, 51–70 (2014)
    https://doi.org/10.1007/s10994-013-5346-7
28. Williams, J.K., Cornman, L., Yee, J., Carson, S.G., Cotter, A.: Real-time remote detection of convectively-induced turbulence. In: AMS 32nd Radar Meteorology Conference, Paper P12R.1 (2005)
29. Williams, J.K., Cornman, L.B., Yee, J., Carson, S.G., Blackburn, G., Craig, J.: NEXRAD detection of hazardous turbulence. In: AIAA 44th Annual Aerospace Sciences Meeting and Exhibit, Paper AIAA 2006-0076 (2006)
    https://doi.org/10.2514/6.2006-76
30. Williams, J.K., Kessinger, C., Abernethy, J., Ellis, S.: “Fuzzy Logic Applications” (Chapter 17). In: Haupt, S.E., Marzban, C., Pasini, A. (eds.) Artificial Intelligence Methods in the Environmental Sciences, 424 pp. Springer, New York (2009)
31. Williams, J.K., Meymaris, G., Craig, J., Blackburn, G., Deierling, W., McDonough, F.: Measuring in-cloud turbulence: the NEXRAD Turbulence Detection Algorithm. In: AMS 15th Conference on Aviation, Range, and Aerospace Meteorology, Paper 2.1 (2011)

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