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Gust Frontlwind Shift Detection Aig R (I DOT/FAAlNR-91/4 Gust FrontlWind Shift Detection /; Aig r"thm for the Terminal Program Director Doppler Weather Radar for Surveillance Washington, D.C. 20591 FAA WJH Technical Center \\Il~ \\\~ ~I\I \~\ \~I ~\ lIW m\ \\\1\ \\1\ ·00026693 National Severe Storms Laboratory 1313 Halley Circle Norman, Oklahoma 73069 November 1989 Final Repon This document is available to the public through the National Technical Information Service, Springfield, Virginia 22161. u.s. Departmenl of Transportation Federal Aviation AdmInistration Notice This document is disseminated under the sponsorship of the u.s. Department of Transportation in the interest of information exchange. The u.s. Government assumes no liability for the contents or use thereof. T.c:hnicnl R~rl)rt Documentation Page 1. R.poll No. 2. Goyernmenl A'C~lliol"\ No. DOT/FAA/NR-91/4 5. Ropo" Dolo Gust Front/Wind Shift Detection Algorithm for November 19R9 the Terminal Doppler Weather Radar 6. P.tforrnlop O';onizDllon Code' MGGOOO h~:""",:,::-:-:;:-:-;----------------------~-----! 8, P.rlo,tning OrgOl'\ilCllion Report No. 7. Aul~orl,) Art h ur U"itt, S teven D. Smith, Michael D. Eilts Laurie G. Hermes and Diana L. Kingle-Wilson 1D. ""O'~ Un,' No. (T RAI5j National Severe Storms Laboratory 1313 Halley Circle 11. ConIrQ" or GraM' Nc. Norman, OK 73069 DTFAOI-SO-Y-IOS24 13. Typ. of R.par! and P."oc Cav...d ~::--::-------------------------i 12. Spon,o,;ng Agency Nom. and Add, ... US Department of Transportation Final Report Federal Aviation Administration 800 Independence Ave, SW 14. 5pof\lorin9 Agrncy Cod~ Washington, DC ANR-1S0 16. Ab'llac' An algor i thm for real-time detection of tornadoes, using single-Doppler radar data, is described. This algorithm searches for tornadic vortex signatures (TVS's) which are characterized by strong azimuthal shear in Doppler velocity fields. A TVS usually indicates that a tornado is occurring. The algorithm searches for azimuthal radial velocity differences, above a certain threshold, between adj acent gates at constant range. It then builds these "pattern vectors" into features for each scan in elevation, and finally determines the vertical correlation among features. When at least three features are vertically correlated and the lowest one is bel 0 ..... a prescribed minimum-height threshold, a "TVS" is declared, indicating that a tornado is occurring. If the lowest feature is not below the height threshold, a "potential TVS (PTVS) ". is declared, indicating that a tornado may soon occur. The TVS algorithm has been tested on five tornadoes that occurred in Colorado and Missouri. Each tornado had an associated TVS, which was detected by the algorithm. In all cases, either a PTVS or TVS preceded each tornado, resulting in a 4 minute average lead time. Evaluated on a scan-by-scan basis, the Probability Of Detection (POD) is 78%. No TVS false alarms and only 3 PTVS false alarms occurred for these 5 cases. The algorithm was also tested on two rotating microbursts with no detections occurring. 17. K. y Wo.d, Gust Front, ~indspeed, Terminal Doppler, Doppler Radar This document is available to the public through the National Technical lnformatio Wea~her, Weather Radar, Weather Algorithm Service, Springfield, VA 22161 21. No. of Pog.. 22. P"ce Unclassified Unclass if ied 76 Form DOT F 1700.7 (<'-72,1 Reproduction of completed pOlle o"Ihori1ed GUST FRONT/WIND SHIFT DETECTION ~LGORITHM FOR THE TERMINAL DOPPLER WE~THER RAD~R Arthur Witt Steven D. smith Michael D. Eilts NOAA/Environmental Research Laboratories National Severe Storms Laboratory Norman, OK 73069 Laurie G. Hermes cooperative Institute for Mesoscale Meteorological Studies University of Oklahoma Norman, OK 73019 Diana L. Klingle-Wilson Massachusetts Institute of Technology/Lincoln Laboratory Lexington, MA 02173 PREFACE We would like to thank Drs. Dusan Zrni6 and Edward Brandes for valuable discussions and suggestions concerning this study. We also thank all those from MIT/Lincoln Laboratory and the National Severe storms Laboratory who assisted in the collection of Doppler radar data. Joan Kimpel provided graphics support and the manuscript was typed by Carole Holder and Kelly Lynn. 1 Table of Contents PREFACE . 1 LIST OF FIGURES iii LIST OF TABLES v ABSTRACT vi 1. INTRODUCTION 1 2. PATTERN RECOGNITION 3 3. FEATURE EXTRACTION 8 4. VERTICAL CONTINUITY 9 5. POLYNOMIAL CURVE FITTING 11 6. HORIZONTAL WIND ESTIMATION ON BOTH SIDES OF A DETECTED GUST FRONT 12 7. GUST FRONT TRACKING AND FORECASTING 17 8. PERFORMANCE ENHANCERS 17 9. TEST RESULTS 20 9.A. PROBABILITY OF DETECTION (POD) 23 9.B. FALSE ALARM RATIO (FAR) 34 9.C. PREDICTION EVALUATION 36 9.0. WIND ESTIMATE EVALUATION 44 10. SUMMARY AND CONCLUSIONS 54 11. REFERENCES 58 APPENDIX A 59 APPENDIX B 64 APPENDIX C 66 ii LIST OF FIGURES Figure 1. Example of a pattern vector. Reflectivity factor Z (dBZ), radial velocity (m S-l), and a nine-point average mean velocity are given. "B" and "E" denote the beginning and ending range of the pattern vector. "G" is the detected position of the front, i.e., the location of the peak shear. The location and length, in range, of each data window used by the wind shift algorithm is also shown (from Witt and smith, 1987). Figure 2. criteria by which a pattern vector is saved (hatched area). Figure 3. Geometry of the vertical continuity box. The uppercase "Els" denote the endpoints of the detected gust front and the lower case "c" denotes the centroid position. Initially determined size and location is dashed and the solid line box is its final location (from Witt and Smith, 1987). Figure 4. Schematic showing detected gust front and the two data windows over which we estimate the horizontal wind. The adjustable parameters are: 6r is the range extent of the window; 68 is the azimuthal extent of the window; and or is the range offset of the window from the detected front (from Witt and Smith, 1987). Figure 5. Example of an algorithm overlay plot. Figure 6. Detections made on 4 September from 1957-2022 UT in 5 minute intervals for a gust front of strong to severe strength. The "+" represents the location of Stapleton Airport. Figure 7. Detections made on 29 July from 2353-0029 UT in 4-5 minute intervals for a gust front of moderate strength. Figure 8. POD, as a function of Percent Convergent Length Detected Threshold (% CLD"'ln) I for moderate, strong, severe and all gust fronts. Figure 9. POD, as a function of Percent Length Detected Threshold (% Lmln) I for moderate I strong I severe and all gust fronts (from Klingle-Wilson, et al., 1989). Figure 10. Detections made on 16 March from 1955-2035 CST in 8-9 minute intervals for a gust front of strong to iii severe strength. The "+11 represents will Rogers Airport. Figure 11. Detections made on 24 May from 2356-0029 CST in 7-9 minute intervals for a gust front of strong to severe strength. The 11+" represents will Rogers Airport. Figure 12. POD, as a function of Percent Convergent Length Detected Threshold (% CLDmin ), for moderate, strong, severe and all gust fronts. Figure 13. Histogram of time differences between la-minute forecasts and actual positions. Negative (positive) values indicate a late (early) forecast. Figure 14. Histogram of time differences between 20-minute forecasts and actual posi~ions. Negative (positive) values indicate a late (early) forecast. Figure 15. Time differences of la-minute forecasts as a function of gust front strength. Category 1 gust fronts are weak, 2 are moderate, 3 are strong, and 4 are severe. The numbers beside the asterisks indicate the number of observations represented by the asterisk. Figure :'6. Time differences of 2o-minute forecasts as a function of gust front strength. category 1 gust fronts are weak, 2 are moderate, 3 are strong, and 4 are severe. Figure 17. Location of mesonet stations, LLWAS sensors, stapleton International Airport runways, and the FAA-Lincoln Laboratory FL-2 radar site. Figure 18. Scatter plot of wind direction computed by the wind shift algorithm versus the average wind direction computed from mesonet data. The solid line represents a one-to-one correlation between the variables. Figure 19. Difference between algorithm direction estimates and average wind directions computed from mesonet data. Figure 20. Scatter plot of wind speeds computed by the wind shift algorithm versus averaged peak wind speed from mesonet data. The solid line represents a perfect correlation between the variables. iv LIST OF TABLES Table 1­ POD and FAR statistics. Table 2. Average Percent of Convergent Length Detected statistics. Table 3. POD and FAR statistics. Table 4. Average Percent of Convergent Length Detected statistics. Table 5. POD and FAR statistics for 10­ and 20-minute forecasts. Table 6. Comparison of the forecast time of frontal passage with the actual time of frontal passage. Table 7. Comparison of algorithm wind estimates with actual wind observations. (U) indicates uniform wind model; (P) indicates perpendicular wind model. Table 8. Comparison of algorithm-generated and mesonet wind speeds. v ABSTRACT During 1987, Doppler radar data were collected in Denver, Colorado and Norman, Oklahoma to test and evaluate the Gust Front Detection Algorithm, which is designed to detect the radial convergence associated with a gust front, forecast its future location, and estimate the wind speed and direction behind the front. This paper describes the version of the gust front algorithm which will be deployed in the initial Terminal Doppler Weather Radars. The algorithm uses two 360· low-elevation angle PPI's of radial velocity
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