1. FAA AWRP-sponsored Turbulence Nowcasts/Forecasts Tri-Agency Review 2 Dec 2010 Robert Sharman NCAR/RAL Boulder, CO USA sharman@ucar.edu Collaborators: Larry Cornman, John Williams, Teddie Keller, Jenny Abernethy, Julia Pearson, Julie Prestopnick, Gary Blackburn, Greg Meymaris, Gerry Wiener, Stan Trier (NCAR), Rod Frehlich (CU/NCAR) Todd Lane (U. Melbourne), Rob Fovell (UCLA), John McHugh (U. New Hampshire), Kris Bedka (SSAI/NASA) Wayne Feltz, Tony Wimmers, Pao Wang (U. W. Madison/CIMSS) Jung-Hoon Kim, Hye-Yeong Chun (Yonsei U.)

2. FAA turbulence products aimed at IOC In situ edr data –EDR = ε 1/3 (m 2/3 s -1 ): atmospheric turbulence metric –Data received in near real time fed into 4D data cube –Used in forecast/nowcast products Turbulence forecast product (Graphical Turbulence Guidance – GTG3) –3D gridded deterministic output of edr –Clear-air sources including MWT –NWP-based Turbulence nowcast product (GTGN)-1 –3D gridded deterministic output of edr –All sources –Observation-based, updated every 15 min –Inputs In situ data NTDA2 CONUS mosaic (edr) DCIT GTG3 NOTE all products provide EDR

3. GTG3 IOC product: satisfies “Segment 1” requirements as specified in IWP WRFRR cutout grids (current RUC13 domain) Upper and midlevels (10,000 ft-FL450) only, no low-levels Includes CAT, MWT, convection sources not explicitly considered 1-18 hr forecasts Uses UAL, DAL, SWA, 767 insitu + PIREPs Uses deterministic combination of diagnostics –Really a weighted ensemble mean –Ds are related to model spatial variations –Ws are determined from performance metric based on comparisons to 100,000s observations –Regionalized and merged using different diagnostic combinations in different areas, perhaps seasonally –Includes diagnostic-edr PDF mappings GTG = W 1 D 1 * + W 2 D 2 * + W 3 D 3 * + ….

4. 10/15/2015 4 Ellrod1 DTF3 FRNTGth VWS UBF Ri CLIMO TEMPG - NVA NCSU1 NCSU2 EDRS10 GTG GTG =Weighted ensemble of turbulence diagnostics 0 h forecast valid at 22 Sep 2006 15Z

5. 10/15/2015 5 GTGProb > lightProb > modProb > severe Use of indices as ensembles provides confidence values (or uncalibrated probabilities) 0 h forecast valid at 22 Sep 2006 15Z Red=.75 Red=.30

6. MWT Funded primarily by NASA ASAP For WRFRR, based on comparisons to MWT pireps, best discriminators for null vs MOG turbulence are: –Best single 2D diagnostic = Umax (in lowest 1500 m) –Best 3D diagnostics = |wmax| (in lowest 1500m) x some measure of temperature variability at flight level (e.g. |∆T|, C T 2, Ri with du/dz from thermal wind) –Different than RUC Not so good discriminators are –Existence of critical level –PIREPs-derived MWT climatology –Model-produced SGS TKE Satellite-derived feature detectors may help (UW-CIMSS) High horizontal resolution should help Nested higher resolution grids Outer domain Lower resolution

7. Conversion of diagnostics (D) to D*(ε 1/3 ) 7 Assume turbulence in the UTLS has a log normal distribution of ε 1/3 Consistent with GASP, research aircraft, and NWP data So rescale diagnostic D to ε 1/3 through Where a and b are chosen to give best fit to expected lognormal distribution in the higher ranges PACDEX data

8. GTGN Output is –deterministic edr (m 2/3 s -1 ) –updated every 15 min. –Gridded data with same horizontal and vertical resolutions as GTG3 Includes –“Direct” observations of turbulence from In situ edr (UAL, DAL, SWA, 767 insitu) Pireps NTDA2.5 CONUS mosaic –“Inferences” from Satellite-derived features from NASA-funded programs –CIT –Possibly MWT GTG3 analyses, 1 or 2 hr forecasts, including MWT lightning

9. GTGN components: PIREPS + in situ turbulence detection of EDR Verbal pilot reports (PIREPS) –Aircraft dependent –Subjective –Position and time inaccuracies Median=98 km, mean=135 km, based on 1400 edr-PIREPs –~ 400/day In situ EDR (= ε 1/3 m 2/3 s -1 ) measurements –Automated: Resides within the avionics system on selected commercial aircraft –Aircraft independent measure of turbulence scale 0-1 –Currently ~ 5000/hour 100 UAL 757s (reported every min in cruise) 80 DAL 737s (“triggered” + “heartbeats” every 15 min) 10 SWA 737-700s for testing. Planned deployment on ~340 total Both used in GTG/GTGN EDR PIREP

10. GTGN components (cont.): NTDA (NEXRAD Turbulence Detection Algorithm) Uses LII spectral width estimates + extensive QC to measure in-cloud EDR 3-D EDR mosaic of 133 NEXRADs running in real-time at NCAR with 5 min updates NEXRAD EDR 2008-08-11 1900, FL360 EDR scale

11. GTGN components (cont.): DCIT Algorithm designed to diagnose regions of in-cloud and near-cloud convectively- induced turbulence (CIT) Uses NWS forecast model data, lightning, NEXRAD, and satellite data along with an empirical model based on in-situ EDR “truth” Funded by FAA, NASA, NOAA Complemented by high resolution computer simulations of CIT events –Revealed the importance of gravity waves and gravity wave “breaking” –Demonstrated shortcomings of current FAA thunderstorm avoidance guidelines 0905 UTC 16 June bands

12. Idealized single sounding – animation of w at z=12 km* 12 *Courtesy Rob Fovell UCLA

13. 4 km w/YSU/LFO4 km w/QNSE/Seifert 1.5 km w/YSU/Seifert 1.5 km w/YSU/LFO More sensitivity studies

14. GTGN Example: Components & Output: 20100813 at 22z FL380 GTG2 1hr Fcst GTGN & Next 15min In situIn situ, Pireps (1 hr prior) & NTDA

15. Research needs/opportunities Need better understanding of turbulence processes –For CAT, define the linkage between the large (observable) scales to aircraft scales –Turbulent processes within cloud (could increase capacity) This can be accomplished through –High resolution numerical simulations of turbulent events have been very instructive –Case studies of turbulence encounters (NTSB or airlines) NTDA EDR dBZ

16. Research needs/opportunities (cont.) Need more and better observations over CONUS and globally –Expand insitu measurements (FAA, NASA, industry?) Need nighttime (package carriers), international coverage, with water vapor Need edr -> aircraft loads maps (PIREPs) Develop turbulence climatologies from in situ (e.g., event duration) –Profilers? –Include onboard radar Use of satellite data to infer turbulence (NASA?) –Explore the benefits of higher-resolution e.g., hyperspectral –Perform coupled atmospheric/satellite simulations for CIT,MWT Develop NWP optimal NWP model configuration (NOAA, FAA?) R&D better methods for combining diagnostics (FAA?) –AI techniques? –Ultimately output needs to be probabilistic