2. Sources of Turbulence

3. FAA Turbulence PDT Turbulence Forecasting Graphical Turbulence Guidance (GTG) –NWP-model-based turbulence forecasts with dynamically-tuned “ensemble of experts” GTG Nowcast (GTGN) En-route Turbulence Automated EDR reports from commercial aircraft –United g-load method –New Southwest/Delta method Turbulence Remote Sensing NEXRAD turbulence detection algorithm (NTDA) –Nationwide radar-based real-time detection Other remote sensors –Satellite data –Profilers –TDWR radar CIT avoidance guidelines –Evaluate current guidelines –Recommend alternatives –Develop “Diagnose-CIT” algorithms

4. Graphical Turbulence Guidance Product The current GTG clear-air turbulence forecast product with overlaid in situ turbulence reports from United Airlines aircraft Operational GTG: http://adds.aviationweather.gov Experimental GTG: http://weather.aero

5. Radar detection of in-cloud turbulence NTDA performs data quality control and produces in-cloud EDR on a polar grid To be installed on NEXRADs, ingested and mosaicked at NCEP using NSSL mosaic algorithm

6. Possible approach to CIT in GTG “Nowcast” (0-30 min?) NTDA Diagnose-CIT (realtime and NWP data) NWP and satellite-based diagnostics with in situ, MDCRS nudging and dynamic tuning Forecast (30 min – 6 hours?) Diagnose-CIT (NCWD conv. wx. forecast and NWP data) NWP and satellite-based diagnostics with dynamic tuning

7. Current avoidance guidelines Don’t attempt to fly under a thunderstorm even if you can see through to the other side. Turbulence and wind shear under the storm could be disastrous. Do avoid by at least 20 miles any thunderstorm identified as severe or giving an intense radar echo. This is especially true under the anvil of a large cumulonimbus. Do clear the top of a known or suspected severe thunderstorm by at least 1,000 feet altitude for each 10 knots of wind speed at the cloud top. Do circumnavigate the entire area if the area has 6/10 thunderstorm coverage. Do regard as extremely hazardous any thunderstorm with tops 35,000 feet or higher whether the top is visually sighted or determined by radar.

8. “CIT Avoidance Guidelines” task goals Evaluate the FAA’s current thunderstorm avoidance guidelines for their effectiveness in mitigating CIT encounters Propose alternative guidelines if appropriate Develop CIT diagnostics module (“Diagnose-CIT”) for GTGN

9. Data sources Turbulence “truth” data –In situ EDR reports from commercial aircraft –TAMDAR? –PIREPs (but may be too imprecise) –Field program data (NASA, IHOP, BAMEX, etc.) –NTSB accident cases, FDR data –NTDA EDR data? Thunderstorm feature data –Radar data (VIL, echo tops, NSSL 3-D reflectivity, NTDA) –Lightning data (NLDN) –GOES satellite data, IR-derived cloud tops –Conv. Wx. nowcast data (NCWD, NCWF) –Aircraft data (winds, temperature, EDR) Environment data –NWP model data and derived diagnostics

10. Available RUC and derived fields Convective Parameters CAPE CIN Showalter Index Totals Indices Lifted Index Precipitable Water SWEAT (Severe Wx Threat Index) K-Index Bulk Richardson Number Richardson Number Lapse Rate DTF3 (Diagnostic TKE Formulations) Vertical Shear Horizontal Shear 1/Stability EDR (Structure Function derived Eddy Dissipation Rates) SIGW (Structure Function derived Sigma Vertical Velocity) Divergence Vorticity Dutton NCSUI (NC State U. Index) Colson-Panofsky Ellrod1 NCSUI (N.C. State U. Index) Saturated Richardson Number Frontogenesis Function LAZ (Laikhman-Alter-Zalik) NGM1 and NGM2 ABSIA UBF (Unbalanced Flow) NVA (Negative Voriticity Advection) Tropopause Height Wind Speed Turbulence Indices

11. Approaches to Diagnose-CIT Detailed case studies Data mining based on comparisons of thunderstorm features and environment data with “truth” data (data mining = discovery of patterns or relationships through the automated analysis of data) –Improve understanding of processes involved –Build/tune automated diagnosis algorithm Fine-scale numerical modeling of interesting cases Feedback between these approaches

12. Early statistical results comparing convection to out-of-cloud turbulence

13. Initial results: evaluating FAA thunderstorm avoidance auidelines For all aircraft turbulence reports over 11 months in summers of 2004 and 2005, computed –horizontal proximity to thunderstorms, indicated by NCWD VIL values (e.g., >= 3.5 kg m -2 ) –vertical proximity to thunderstorm tops, indicated by radar echo tops Stratified data by proximity values Determined frequency of different levels of turbulence in each proximity range and compared to average frequency to get “risk”

14. Results: Horizontal proximity Turbulence categories:

15. Results: Vertical proximity Turbulence categories:

16. “Dartboard” comparisons Motivations –Investigate turbulence severity dependence on whether convection is upwind or downwind –Investigate turbulence dependence on intensity and size of convective activity Approach –For each aircraft measurement, orient “dart board” based on aircraft wind direction (5-10, 10-20, 20- 40, 40-80, 80-160 nmi rings, 60  wedges) –For each in situ measurement, compute distance to convection within each wedge (convection given by NCWD VIL) coverage by convection within wedge “rings”

17. Wedge orientation Wedge 0 Wedge 1 Wedge 2 Wedge 5 Wedge 4 Wedge 3 Wind vector

18. “Dartboard” 5 10 20 40 80 160

19. Wedge distance to convection (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

20. Wedge distance to convection (alt > 20kft) Avg. EDR = 0.05 (null) Avg. EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

21. Coverage: 5-10 nmi (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

22. Coverage: 10-20 nmi (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

23. Coverage: 20-40 nmi (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

24. Coverage: 40-80 nmi (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

25. Coverage: 80-160 nmi (alt > 20kft) Peak EDR = 0.05 (null) Peak EDR >= 0.35 (MoG) Red: VIL = 0.9 kg m -2 Orange: VIL = 5 Green: VIL = 10 Lt. Blue: VIL = 15 Blue: VIL = 30

26. Learning predictive algorithm: random forests Basic idea –“grow” multiple decision trees to predict turbulence based on “dartboard” values, each using a random subset of data (“bagging”) and random splitting variables –trees function as “ensembles of experts” –trees “vote” to determine consensus categorization; they also create a “probability distribution” over classes Vote: 4Vote: 2Vote: 4 Vote: 1 => consensus vote: 4 (“confidence” 3/5)

27. Initial results (ROC curves) Discriminate light or greater (in situ  1/3 > 0.1 m 2/3 s -1 ) vs. null turbulence (random and biased summertime data training samples, with and without wind magnitude)

28. Initial results (ROC curves) Discriminate moderate or greater (in situ  1/3 > 0.3 m 2/3 s -1 ) vs. less than moderate turbulence

29. Next steps Interpret data mining results to understand importance of different variables Develop an “augmented in situ database” with quality-controlled EDR values, RUC model, and variables derived from RUC model data Perform “dart board” comparisons that incorporate cloud top information for each region, use RUC model winds for orientation, and incorporate augmented in situ data Add temporally-lagged data to the analysis Incorporate thunderstorm “objects” and features Use results from case studies and simulations to modify/refine approach

31. Challenges Large number of data sources Analysis/tracking/extrapolation/feature identification from multiple 4-D data fields required –“Curse of dimensionality” In situ EDR data not ideal “truth” Many different sources and conditions, and their interactions, cause observed turbulence Aircraft data are not representative samples of the atmosphere Need additional data sources –“echo tops” data are only to nearest 5,000 ft –object information (size or severity) of nearby storm –history and track of the storm? –environmental conditions that may affect propagation of turbulence, breaking of “gravity waves” Need physical basis for narrowing down fields/features to include in analysis