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Global Atmospheric Turbulence Decision Support System for Aviation

Global Atmospheric Turbulence Decision Support System for Aviation. John Williams, Bob Sharman, and Cathy Kessinger (NCAR); Tony Wimmers and Wayne Feltz (UW/CIMSS). NASA Applied Sciences Weather Program Review Boulder, CO November 18, 2008. and UW-Madison SSEC/CIMSS.

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Global Atmospheric Turbulence Decision Support System for Aviation

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  1. Global Atmospheric Turbulence Decision Support System for Aviation John Williams, Bob Sharman, and Cathy Kessinger (NCAR); Tony Wimmers and Wayne Feltz (UW/CIMSS) NASA Applied Sciences Weather Program Review Boulder, CO November 18, 2008 and UW-Madison SSEC/CIMSS

  2. Global Turbulence project goals Develop global probabilistic turbulence nowcasts and forecasts and supporting global convective nowcasts Address clear-air turbulence (CAT), convectively-induced turbulence (CIT), and mountain-wave turbulence (MWT) 0-3 hour nowcasts of convection 0-36 hour forecasts of turbulence, 10,000 – 45,000 ft. Support World Area Forecast System (WAFS) Provide improved accuracy and timeliness over current SIGMETs and SIGWX charts Support NextGen SWIM 4-D data cube Leverage and provide outlet for previous FAA and NASA-funded work (e.g., Oceanic Weather)

  3. Approach Develop a database of global turbulence reports for empirical model development, tuning and verification Adapt CONUS GTG (AWRP-funded) diagnostics for use with Global Forecast System (GFS) model data Adapt Oceanic Nowcast (AWRP/NASA) for global application on GOES East and West, MTSAT, and Meteosat Adapt satellite-based turbulence algorithms (NASA/GOES-R) including tropopause folding, CIT and MWT features Adapt CIT diagnostics (AWRP/NASA) for use with GFS model and satellite convection diagnoses and nowcasts Develop nowcast/forecast data fusion system Verify and benchmark the forecasts vs. SIGMETs Run real-time demonstration over GOES East/West domain Adapt CONUS Experimental ADDS display (AWRP/NOAA) Cockpit uplink messages (prev. AWRP-funded) Web-based pilot/forecaster feedback

  4. Flow Chart for Global GTG Integration Global Graphical Turbulence Guidance Convection Diag. & Now. Geo-, Leo- Satellites CIT/CAT Diagnoses GFS Model Turb. Detection (trop. folding) In Situ Observations (tuning) Mountain Wave

  5. Global GTG Demonstration at NCAR Global GTG ATM / Dispatchers Synthetic SIGMETs Pilots World Wide Web

  6. Future NEXTGen Use of Global GTG WAFC Human over the Loop Global GTG ATM / Dispatchers Pilots SWIM NEXTGen 4-D Data Cube SIGMETs World Wide Web

  7. Cockpit Uplink Demo Future Positions Flight Path Current Position ASCII display via cockpit printer • Aircraft-relative display of cloud top height • Pilot and dispatcher receive a “heads up” for approaching weather (common situational awareness) • United Airlines test on oceanic flights Cloud Top Height >40kft 30-39kft

  8. Empirical Turbulence Data: UAL EDR United EDR above 10,000 ft MSL, 07-01-2008 to 07-15-2008

  9. Empirical Turbulence Data: Delta EDR Delta EDR above 10,000 ft MSL, 07-01-2008 to 10-31-2008

  10. Empirical TurbulenceData: Ude Ude, various airlines, 11-1-2008 to 11-5-2008

  11. Empirical Turbulence Data: AIREPs

  12. Turbulence data collection status Most data are available from NCAR archives NCDC archive data may also be used Quality control development is underway Database design is underway

  13. Adapting GTG diagnostics for GFS Challenges of global model Cycle boundaries in longitude Poles are singular points Constant lat-lon grid, therefore Δx (long.) nonuniform in spatial distance, much smaller near poles Different vertical coordinates Some diagnostics break down at equator RUC (hybridB coordinates) GFS (sigma coordinates)

  14. GFS-GTG 6-hr forecasts valid 18Z 4 Nov2008 (election day) Ellrod index FL350 EDR index FL350 Ri from thermal wind FL200 Note: breaks down at equator GFS-based diagnostic RUC-based diagnostic

  15. GFS-GTG status Initial implementation of common GTG core software (supports RUC, WRF, and GFS models) Still testing GFS-based diagnostics Need to refine based on turbulence reports for particular cases Then perform statistical evaluations over extended time period GFS data are being ingested and converted in real-time 0.5 degree GFS 00-, 03-, 06-, 09-, and 12-hour pressure coordinate files (will switch to 1/3 degree) Real-time system monitoring tools have been set up

  16. (Near-) Global Convection Nowcasting Courtesy of David Johnson MTSAT-1R • Geostationary satellite-based methodologies cover +70 degrees latitude • Could expand coverage using polar-orbiting satellites • Northern latitudes benefit (over-the-pole aviation routes) • Few (no?) commercial aviation routes over Antarctica

  17. Geo-Satellite Methodology Cloud Top Height Global Convective Diagnosis Convective Diagnosis Oceanic Interest • Fuzzy logic data fusion of two methods • Cloud Top Height from longwave infrared (10 micron) & GFS model • Global Convective Diagnosis [TB(6.5 micron) – TB(10 micron)] • Convective Diagnosis Oceanic (CDO) Interest Field (0 to 2) • Final, binary product will have a threshold applied

  18. Challenges for Convection Nowcasting • Varying, full-disk satellite scanning strategies • 3 hourly: GOES-E and GOES-W • 1 hourly: MTSAT-1R • 15 minutes: Meteosat-9 • GOES satellites have partial scans that could be merged • Discontinuities across scanning boundaries make extrapolation difficult • Planning for 3-hourly updates for Global Turbulence • Provides an analysis and 3-hr nowcast • Will examine Meteosat-9 imagery in anticipation of future rapid-update GOES-R capability

  19. Global convective nowcasting status Real-time ingest and processing of GOES full disc scans (Terrascan) implemented Initial (uncalibrated) CDO field produced in real-time Archived data from MTSAT has been transmitted from CIMSS to NCAR for analysis Real-time system monitoring tools have been set up

  20. Tropopause Fold Turbulence Prediction(Tony Wimmers, UW/CIMSS) • Background • Algorithm basics • Adapting the algorithm to aircraft hazard awareness • Latest developments 20

  21. Background

  22. 14 150 stratosphere 12 200 subtropical air mass 10 300 Pressure (hPa) Height (km) 8 tropopause 400 6 500 600 4 polar air mass 700 (~100 km) front Background: Tropopause folding and Clear Air Turbulence (CAT) Upper-air front From Shapiro, M. A. (1980): Turbulent mixing within tropopause folds as a mechanism for the exchange of chemical constituents between the stratosphere and the troposphere, J. Atmos. Sci., 37, 994-1004.

  23. Background: GOES Layer-Average Specific Humidity (GLASH) (WV channel) (GLASH product) • Wimmers, A. J., and J.L. Moody, A fixed-layer estimation of upper tropospheric specific humidity from the GOES water vapor channel: Parameterization and validation of the altered brightness temperature product, Journal of Geophysical Research-Atmospheres106 (D15), 17115-17132, 2001.

  24. Background: Synoptic-scale UTH

  25. Background: Synoptic-scale UTH 300 hPa Potential Vorticity

  26. Background: Validation with aircraft ozone lidar Tropopause folds were measured at crossings of upper-troposphere air mass boundaries • Wimmers, A. J., J.L. Moody, E.V. Browell, J.W. Hair, W.B. Grant, C.F. Butler, M.A. Fenn, C.C. Schmidt, J. Li, and B.A. Ridley, Signatures of tropopause folding in satellite imagery, Journal of Geophysical Research - Atmospheres, 109, art. no. 8360, 2003.

  27. Algorithm basics

  28. 1. Humidity product(cloud masked) decreasing specific humidity latitude longitude

  29. 2. Smoothed gradient of humidity product decreasing specific humidity latitude latitude longitude longitude

  30. latitude longitude 3. Contour along boundaries decreasing specific humidity latitude latitude longitude longitude

  31. latitude longitude 4. Extend polygons out from boundaries decreasing specific humidity latitude latitude • Wimmers, A. J. and J. L. Moody, Tropopause folding at satellite-observed spatial gradients: 2. Development of an empirical model, Journal of Geophysical Research – Atmospheres, 109, art. no. D19307, 2004. longitude longitude

  32. Adapting the algorithm to aircraft hazard awareness

  33. Adapting to aircraft hazard awareness Validation data - Eddy Diffusion Rate Automated reporting of inertial disturbance on commercial aircraft 3-minute integration time per measurement (short). This indicates several measurements through any single turbulent region. Collected and quality-checked by NCAR ~ 1 month latency 33

  34. x x 1. Filter for the trop. folds associated with turbulence x x Adapting to aircraft hazard awareness decreasing specific humidity 2. Assign a vertical height to the trop. folds latitude longitude

  35. Filtering cases of tropopause folding Measurements of turbulence are limited to near-orthogonal crossings of the aircraft with the direction of flow “20º” “75º” • Also limiting factors: • Direction of flow • WV gradient strength • Time of year (possibly)

  36. Height assignment(validation with EDR data) Predicted area of turbulence Potential temperature difference from the lower tropopause Distance from tropopause break probability of turbulence • EDR reports conform to the expected cross-section of turbulence in a tropopause fold

  37. Recent developments

  38. Algorithm Development - Adaptation for operational use In 2007, the Tropopause Folding Turbulence Product was adapted as an algorithm for the GOES Algorithm Working Group (AWG) AWG is a multi-year project to prepare end-user products to be optimized and online at the very start of the GOES-R operations The transition of this algorithm to the GOES-R platform includes the following: Port original Matlab code to Fortran () Incorporate products into GEOCAT environment () Apply to synthetic GOES-R 6.19um channel data Validation of the adapted product with EDR data 38

  39. Algorithm Development - Adaptation for operational use Test case output: Tropopause Fold minimum/maximum heights

  40. Algorithm Development - Adaptation for operational use Test case output: Tropopause Fold exposed flight directions

  41. Algorithm Development - Future objectives Adapt the algorithm for use with MTSAT and Meteosat imagery Makes the product truly global Gradients will be intercalibrated with MODIS data and tropopause features can be confirmed with the Aqua Ozone Mapping Instrument (OMI) 41

  42. Summary (John Williams) • The Global Turbulence DSS project aims to produce global probabilistic turbulence nowcasts and forecasts supported by global convective nowcasts • Improve WAFS SIGMET and SIGWX charts • Provide turbulence information for pilots and air-traffic management via the NextGen 4-D data cube • Leverages existing CONUS systems and NASA- and FAA-funded turbulence and convection R&D • Progress made on empirical turbulence database, GFS-GTG, global convection diagnosis, and tropopause fold detection. • An example of a comprehensive, end-to-end, collaborative product development project • NextGen transition to operations needs to be defined

  43. Integrated System Solution (ISS) Diagram

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