1. New developments in the Graphical Turbulence Guidance (GTG) product
Aviation Turbulence Workshop
NCAR 28 Aug 2013
Robert Sharman
NCAR/RAL
Boulder, CO USA

2. Turbulence forecasting – What are we predicting?
“Aircraft scale” eddies that affect aircraft
Aircraft response is aircraft dependent but this is what pilot reports: “light”, “moderate”, “severe”
CANNOT forecast these levels for every aircraft in the airspace
Instead need atmospheric turbulence measure (i.e. aircraft independent measure)
We forecast EDR (= ε1/3 m 2/3s-1 )
ICAO standard
Can relate to airborne and remote EDR estimates
Can relate EDR to aircraft loads (σg ~ ε1/3 )
Convenient scale 0-1
For reference ICAO standard thresholds (2001,2010 ) for mid-sized aircraft are
EDR=0.10, 0.3, 0.5 for “light”, “moderate”, “severe”, resp.
EDR=0.10, 0.4, 0.7 for “light”, “moderate”, “severe”, resp.
EDR
PIREP

3. Graphical Turbulence Guidance (GTG)
Computes an ensemble of turbulence diagnostics (Ds) from NWP model output
Each D is converted to EDR (D*)
GTG=Ensemble weighted mean of D*s
Typically Ds in suite
Contours displayed on ADDS as EDR from ICAO (2001) recommendation:
Current version is GTG2.5, will be upgraded (GTG3) in late 2014 to include low-levels and MWT
Common Fortran77 core to compute indices (AR WRF, WRF RAP, RUC, GFS, UKMET, ECMWF, COSMO)
GTG = W1D1* + W2D2* + W3D3* + ….
Ensemble mean available on
Operational ADDS (GTG2.5) (
0-12 hr lead times, updated hourly
10,000 ft-FL450

4. Some common turbulence diagnostics
Frontogenesis function (good at upper levels)
Dutton Empirical Index
Unbalanced flow (Koch et al., McCann, Knox et al.)
Deformation X shear (Ellrod)
Eddy dissipation rate (ε1/3) computed from second order structure functions of velocity and/or temperature

5. GTG ε1/3 (EDR)CAT, MWT 31 Dec 2011 6-hr fcst valid 18 UTC FL290
WRF RAP
GTG CAT
GTG MWT
GTG MAX(CAT,MWT)

6. Conversion of diagnostics to EDR
EDR distributions are observed to be ~ log-normal
Fit diagnostic distributions (PDF) to observed EDR distributions (aircraft data)
“Moderate”
“Severe”
PACDEX
<logεw1/3 >=-2.7 SD[log εw1/3 ] = 0.27
DAL in situ EDR data
<logε1/3 >= SD[log ε1/3 ] = 0.57

7. Conversion of diagnostics to EDR (cont.)
Rescale diagnostic D to EDR
Where a and b are chosen to give best fit to expected lognormal distribution in the higher ranges
a and b depend on 7

8. Relation of EDR to PIREPs for medium-sized aircraft (B737,B757)
Compared PIREPs to EDR data
3.8 min
70 km
1200 ft
Fit medians with EDR=C P2 :
DAL: C=0.0143
UAL: C=0.0138
ICAO thresholds:
Light (2) = 0.10
Moderate (4) = 0.40
Severe (6) = 0.70
New Threshold Values based on data
(median values):
Light (2): 0.06 (<0.1)
Moderate (4): 0.22 ( )
Severe (6): 0.5 ( )
Courtesy Julia Pearson

9. Relation to other aircraft
severe
Courtesy Larry Cornman

10. GTG3 POD curves 6-hr fcst (2 years) using both PIREPs and in situ EDR data + 60 min from forecast valid time
High h>20,000 ft
Mid h=10-20,000 ft
Low h<10,000 ft

11. GTG MWT POD curves High h>20,000 ft Mid h=10-20,000 ft
Low h<10,000 ft

12. Global GTG GFS UKMET ECMWF
GTG output based on 3 global models for the same case. Contours are EDR (m2/3 s-1) at FL290
31 Dec hr fcst valid 18Z
All models used native vertical grids
All models used same number of diagnostics
All models used same thresholds

13. 0 h forecast valid at 22 Sep 2006 15Z
Use of indices as ensembles provides confidence values (or uncalibrated probabilities)
GTG
Prob > light
Red=.75
Prob > mod
Prob > severe
Red=.30
Red=.30
4/27/2017
0 h forecast valid at 22 Sep Z

14. Turbulence nowcast product (GTGN)
Numerical
Weather
Prediction
Model
Gridded Forecasts
Turbulence
EDR Forecast
Model
(GTG)
Turbulence
inferences
DCIT
algorithm
Real-time
Turbulence
observations
Satellite
features
Turbulence
EDR Nowcast
3D grid
(GTGN)
Airborne observations
In-situ EDR
PIREPs
Lightning
Aircraft
Deviations
ASDI, ADSB
Ground-based
observations
NTDA mosaic
Update interval 15 min

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

16. Summary and future work
Using an ensemble of turbulence diagnostics (GTG) instead of one diagnostic gives more robust performance
Technique can be used with any input NWP model
GTGN being developed, initial results are promising
GTG AUC 0.845, GTGN AUC 0.897
Longer term goals
Provide probabilistic forecasts
Ensembles (NWP + indices)
Logistic regression or random forest
Probability of what?
Forecast convective turbulence
Include satellite feature detectors in GTGN
This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA. 16