ASAP Turbulence Research at NCAR

1. NASA Applied Science Program Review Session 2B: Turbulence NCAR/RAL Boulder, CO USA ASAP Turbulence Research at NCAR Mountain Wave Turbulence Bob Sharman & David JohnsonNCAR

2. Background – known turbulence sources Clear-air Turbulence (CAT) Cloud-induced or Convectively-induced Turbulence (CIT) Mountain wave Turbulence (MWT) In-cloud turbulence Low level Terrain-induced Turbulence (LLT) Convective boundary Layer turbulence Source: P. Lester, “Turbulence – A new perspective for pilots,” Jeppesen, 1994

3. Turbulence Forecast Product: Graphical Turbulence Guidance (GTG) Based on RUC NWP forecasts Uses a combination of turbulence diagnostics, merged and weighted according to current performance (pireps, EDR) (Mainly) clear-air sources 10,000 ft MSL-FL450 V 1.0 on Operational ADDS since Mar 2003 V 2.0 on Experimental ADDS since Nov 2004 Independent QA RT performance evaluations Current work areas Probabilistic forecasts of moderate-or-greater (MOG) and severe-or-greater (SOG) turbulence Optimal use of insitu reports Assimilation of turbulence-related observations Develop diagnostics for other turbulence sources (MWT, CIT,..) Transition to WRF … and GTG-N 3

4. GTG process Ingest full resolution grids GTG3/ITFA core Real-time In-situ data In-situ QC Future Compute & threshold turb. diagnostics Satellite features wind profilers 88D edr Real-time lightning flash Score & combine diagnostics Real-time PIREPs ADDS displays Real-time (RTVS) and post-analysis verification

5. Mountain wave turbulence (MWT) • For the past 2 years ASAP work has concentrated on development of a nowcast/forecast system for MWT • A major source of severe turbulence encounters • Related to topographically generated gravity waves (mountain waves) which may “break” causing turbulence MWT Source: P. Lester, “Turbulence – A new perspective for pilots,” Jeppesen, 1994

6. Turbulence climatology – increased levels near mountains (15 years of PIREPs) Canadian Rockies Canadian Rockies Wasatch Range Wasatch Range Sierra Nevada Sierra Nevada Colorado Rockies Colorado Rockies [SOG/Total] PIREPs 1-60,000 ft 1993 – 2007 Topography Mog/Total 6

7. Turbulence levels are significantly higher over mountainous terrain • Denver, CO and the Front Range have statistically higher levels of turbulence than almost anywhere in the U. S. • “We are gonna get there but it’s going to be a little rocky. It’s sort of like flying into Denver – you know you are going to land, but it’s not fun going over those mountains.” • President-elect Barack Obama in a campaign speech on the economy at Westminster CO, 29 Sep 2008.

8. Observations also show high incidence of gravity waves over the Western U. S.

9. ASAP MWT forecasting goal/approach • Goal: Develop MWT nowcast/forecast system for aviation use • Approach • Develop a system that integrates observations (including satellite) and NWP model data to provide nowcasts/forecasts of MWT • CIMSS/UAH provides satellite-based gravity wave feature identifier • But NOTE: waves may or may not be turbulent! • NCAR develops semi-empirical MWT forecasting approach based on • PIREPs climatology • Observations • RUC-based (later WRF) diagnostics • NCAR develops integrator and implements as a component of the FAA AWRP sponsored GTG3 and GTG-N • GTG and GTG-N will populate the SAS of the NextGen 4D data cube

10. 4D Weather Data Cube 4D Wx SAS Connection to NextGen 4D data cube • EN-2430 Weather Forecasts - Consolidated Turbulence - Level 1.Near-term predictive models and current weather observations are fused to provide a consolidated turbulence forecast that is available to users over a network-enabled infrastructure. This capability will include North America from 10,000 feet to FL450, 0-18 hours, updated hourly, and will forecast clear air and mountain wave turbulence. ASAP HOL (MDL?) NWP Model (s) NOAA PIREPS Indices GTG FAA AWRP Turb RT Insitu data QC FAA AWRP other RT DCIT GTGN NSSL 3-D DBZ NASA AvWx Radar Mosaic NTDA Primary funding source color code Feature extractor CoSPA Satellite data 24X7 Processing center Red  “Single Authoritative Data Source” (SAS)

11. MWT diagnostics • Provide Identify preferred regions from climatology of MWT PIREPs (15 years) • Develop MWT/total ratios by month, 5000 ft altitude band, CONUS domain • Develop model-based (currently RUC20) diagnostics to compare to MWT pireps [2 years of historical data] • Need to be altitude dependent, so traditional 2d indicators developed by airlines are insufficient: • Strong wind component normal to ridge • Terrain characteristics (mean height, variance, etc.) • Thus requires 3D discriminators

12. Semi-empirical MWT forecasting approach • Identify mountain wave related turbulence events from PIREPs: • Use only records that mention turbulence level and waves, e.g., • UA /OV RLG/TM 1418/FL150/TP C172/WV 30050KT/TBNEG/RM TREMENDOUS MTN WAVE • UA /OV SUN360035/TM 1837/FL125/TP PA31/TA M10/TBMOD/RM MTN WAVE • UUA /OV MVA 085050/TM 1835/FL400/TP B737/TBSEV/RM SEV MTNWAVE/FULL TILT ON THROTTLES. +/- 40KTS • Don’t use “light” reports – attempt to discriminate only between null and moderate-or-greater (MOG) • For now restrict to 10,000 ft to 60,0000 ft • Note turbulence ≠ waves!! – Not trying to predict wave amplitude!!

13. MWT pireps climatology Evaluate over Western U.S.: Example MWT POLYGON h=1km # MWT MOG PIREPs sfc-60,000 ft 1993 – 2007 (15 yrs) % MWT MOG/Total PIREPs 30,000-35,000 ft February 1993 – 2007 (15 yrs)

14. ROC null-MOG performance of 47 diagnostics evaluated against MWT PIREPs • Jun 2005-Jun 2007 • 15Z,18Z • 0,6 hr forecasts • 2985 pireps • 2393 nulls • 592 MOG MWT • Ellrod TI1 • Wmaxt • Standard GTG • MWTClimo x wmax x EDRLL [ CWEDR ] • DIV + DIVT + SIGWX • + wmax + climo + EDRLL No skill line 0-hr fcst High threshold Low threshold

15. CWEDR + Current experimental GTG2 Mountain Wave GTG3 is Combination of GTG+ MWT diagnostic (CWEDR) GTG3 CASE STUDY

16. CASE STUDYExample:Severe Turbulence encounter 15 Mar 2006 GTG2 did NOT capture event Location turbulence encounter (black circle with red center) GTG forecast moderate turbulence (yellow regions) initialized 18 UTC; 3 hr forecast

17. Turbulence encounter (black circle with red center) Mountain wave turbulence - enhanced GTG3: Did capture 15 Mar 2006 severe turbulence event!! Severe turbulence predicted in red regions initialized 18 UTC; 3 hr forecast

18. Another possible MOG discriminator: Wave pattern complexity? Some evidence that turbulence may be related to complexity of lee wave pattern as observed in satellite imagery (Uhlenbrock et al.,2007) Simple wave pattern 3 Sep 2004 2010 UTC Complex wave pattern 6 Mar 2004 1950 UTC MODIS WV (6.7 u) imagery Courtesy Wayne Feltz, CIMSS/SSEC, UW Madison

19. Summary/Future work • Have developed a MWT diagnostic that seems to be fairly reliable in discriminating between smooth conditions (with or without waves) and MOG turbulence due to wave breaking • Extra discrimination may be possible by • Using random forest or other artificial intelligence techniques to come up with a better set of NWP-based diagnostics • Incorporate UAH/CIMSS wave feature detector • Can be used to identify wave and nonwave days and possibly to infer amplitudes • Wave patterns could possibly be used to identify conditions conducive to turbulence • Then incorporate the new algorithm into GTG3!