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TPAWS Certification Data Sets Box-1 Activity

TPAWS Certification Data Sets Box-1 Activity Fred H. Proctor, James F. Watson, and David W. Hamilton NASA Langley Research Center Bob Sharman and John Williams NCAR and George Switzer RTI. ATDS Workshop #5, September 19-20, 2002 Long Beach, CA. Outline Certification Methodology

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TPAWS Certification Data Sets Box-1 Activity

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  1. TPAWS Certification Data Sets Box-1 Activity Fred H. Proctor, James F. Watson, and David W. Hamilton NASA Langley Research Center Bob Sharman and John Williams NCAR and George Switzer RTI ATDS Workshop #5, September 19-20, 2002 Long Beach, CA

  2. Outline • Certification Methodology • Delivered Model Data Sets • -- Dickinson • -- Event 191-06 • Issues • -- Isotropy vs Anisotropy • -- Limited Sample Size • Comparison with VK-Turbulence • Comparison with Flight Data • Summary

  3. FY02 TPAWS Certification MethodologyOverview • TPAWS Milestone#17 specifically relating to Certification was added to the FY02 WxAP Level-2 Plan; action from Workshop#3. • Developed and began implementation of the 6-box chart “Certification Methodology”, 25-Sep-01. • FY02 budget augmentation was requested and denied. • Prioritized Boxes 1, 2,3, 6-Flight Deck. • Prelim status report, actions from Workshop#4. • Small mid-year budget “ear-mark” for Box 4 weight issues, and Box 6 Scoring rules. JFW

  4. FY02 TPAWS Certification MethodologyOverview • While a high priority in FY02, 757 Flight Campaign and resulting data analyses pushed back some efforts. • Also, budget increase was not as large as requested and came at mid-year. • Milestone#17 was slipped into FY03 and based upon AvSp/WxAP milestone adjustments will roll-up into another new Lvl-2 milestone in FY03/04. • Certification is FY03 top priority for TPAWS, • General “planned” TPAWS budget increases slightly over FY02. • FY03 757 WxAP Flight Campaign has been de-scoped. JFW

  5. Certification Methodology Aircraft track Reflectivity, wind in radar volume 3d grid volume Simulated cloud NCAR

  6. + Cloud model grid Von Karman turbulence grid Simulation Methodology • Use cloud models to simulate winds and reflectivity • High resolution difficult to obtain while resolving the volume around a large cloud • Sub-volume selected which encompasses turbulence event • Von Karman representation of turbulence assumed for small scales not resolved by cloud model • Fine-grid turbulence generated by locally modulating the von Karman intensities by the large-scale resolved motion

  7. Data Set Analysis/Hazard MetricFor any horizontal plane in the TASS data set, a w is computed using a moving average as: where the averaging interval is Lx=Ly = 1000 m, w is vertical wind, and

  8. Data Set Analysis (continued) Substitute u(x,y) and v(x,y) for w to get u andv , respectively. For a particular aircraft, RMS normal load can be estimated from w using look-up tables; i.e., ng(x,y) = F{w , altitude, aircraft type, weight, airspeed}

  9. Two Model Data Sets Available • Dickinson ND • Severe turbulence encountered by B-757 at 11 km AGL on 10 July 1997 • Event 191-06 • Severe turbulence encountered at 10.3 km AGL on 14 Dec 2000 during NASA’s TPAWS flight tests

  10. DICKINSON SIMULATION -- DATA SET • Physical domain size and vertical location • 14,196 x 14,196 meters horizontally • 6183.75 meters vertically starting at 7700 meters AGL • Number of grid points • 512 x 512 horizontally • 256 vertically • Grid resolution • 27.78 meters in both horizontal directions • 24.25 meters in vertical direction • Variables • U, V, W, and radar reflectivity factor

  11. Y (km) Y (km) X (km) X (km) Horizontal Cross-Section of Dickinson Data Set radar reflectivity RMS acceleration from dBZ 11 km elevation RMS G dBZ 8 km elevation RMS G GFS/FHP 9/2002

  12. radar reflectivity Y (km) Y (km) X (km) X (km) Horizontal Cross-Section of Dickinson Data Set 11 km elevation dBZ m/s m/s m/s GFS/FHP 9/2002

  13. Horizontal Cross-Section of Dickinson Data Set 8 km elevation radar reflectivity dBZ Y (km) m/s Y (km) m/s m/s GFS/FHP 9/2002 X (km) X (km)

  14. EVENT 191-6 SIMULATION -- DATA SET • Physical domain size and vertical location • 12,775 x 12,775 meters horizontally • 3175 meters vertically starting at 7700 meters AGL • Number of grid points • 512 x 512 horizontally • 128 vertically • Resolution • 25 meters in all coordinate directions • Variables • U, V, W, and radar reflectivity factor

  15. TASS Simulation of Convective Lineviewed from southeast(cloud/precipitation surfaces)

  16. Y (km) Y (km) X (km) X (km) Horizontal Cross-Section of FLR 191-6 Data Set radar reflectivity RMS acceleration from dBZ 10.3 km elevation RMS G dBZ 8.3 km elevation GFS/FHP 9/2002

  17. Y (km) Y (km) X (km) X (km) Horizontal Cross-Section of FLR 191-6 Data Set 10.3 km elevation radar reflectivity dBZ m/s m/s m/s GFS/FHP 9/2002

  18. Horizontal Cross-Section of FLR 191-6 Data Set 8.3 km elevation radar reflectivity dBZ Y (km) m/s Y (km) m/s m/s GFS/FHP 9/2002 X (km) X (km)

  19. Example: Isotropy in simulation • These spectra, derived from a N-S path near the “hotspot” in the NASA 191-06 simulation, are nearly isotropic: ¾Sw = 1.1 Su NCAR

  20. Example: Anisotropy in simulation • These spectra, from a N-S path near the “hotspot” in the NASA 191-06 simulation, yield an anisotropic ratio similar to the aircraft flight results: ¾Sw = 2.2 Su NCAR

  21. Example: NASA 191-06 test flight • These spectra are anisotropic: ¾Sw = 2.1 Su . NCAR

  22. Von Karman Data Set Input:  = 4.2 m/s, L = 500 m • Physical domain size and vertical location • 12,775 x 12,775 meters horizontally • 3175 meters vertically • Number of grid points • 512 x 512 horizontally • 128 vertically • Grid Resolution • 25 meters in all coordinate directions • Variables • U, V, and W

  23. Horizontal Cross-Section of Von-Karman Data Set RMS G Y (km) m/s Sample Path Y (km) m/s m/s GFS/FHP 9/2002 X (km) X (km)

  24. Flight Path Through Von-Karman Data Set RMS G GFS/FHP 9/2002 Distance (km)

  25. Flight Path Through Von-Karman Data Set Local Standard Deviation (m/s) GFS/FHP 9/2002 Distance (km)

  26. Summary • Model + subgrid merger completed for NASA 191-06 and Dickinson turbulence encounters • Results of model + subgrid appear reasonable and are supported by flight data • The statistical variability of single events is large • Local correlation between sigma-u and sigma-w affected by anisotropy and event size

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