The Graphical Turbulence Guidance (GTG)The Graphical Turbulence Guidance (GTG) system &

recent high resolution modeling studiesrecent high-resolution modeling studies

A iation & T rb lence in the Free AtmosphereAviation & Turbulence in the Free AtmosphereRoyal Meteorological Society Meeting at Imperial College,

London15 Jan 2014

Robert SharmanNCAR/RAL

Boulder, CO USAsharman@ucar.edu

Aviation turbulence r&d areas• Better observations of aircraft scale turbulence

– In situ turbulence measurement methods– Ground-based and airborne remote sensing techniques,

including satellite-based technologies

• Better nowcasting & forecasting products– Forecast products for strategic planning

• Concentration has been on upper-level CAT MWTConcentration has been on upper level CAT, MWT– Diagnosis and nowcast products for tactical avoidance

• Use of observations to nudge short-term forecasts

• Better understanding of turbulence mechanisms– Analyses of high-rate data gathered in field programs

(airborne and surface)(airborne and surface)– Case studies using high-resolution simulations

• 3D• Controlled environment• Can be used to formulate and test turbulence diagnostics

GTG Forecasting Procedure*

• Aircraft turbulence is much smaller than present operational NWP model

l tiresolutions

• Therefore cannot directly predict aircraft scale turbulence - only hope is to inferscale turbulence - only hope is to infer turbulence potential from larger scale inhomogenities

• Compute “turbulence diagnostics” from coarse resolution operational nwp model

M lti l i lti l f ti• Multiple causes require multiple forecasting strategies →

• Graphical Turbulence Guidance Product

Ensemble mean available on Operational ADDS (GTG2.5)

(http://aviationweather.gov/adds) Graphical Turbulence Guidance Product (GTG) = Ensemble mean of diagnostics

• Available 24x7 on NOAA’s ADDS website

0-12 hr lead times, updated hourly10,000 ft-FL450

*Sharman et al. Weather & Forecasting 2006

Some common turbulence diagnostics• Frontogenesis function (good at upper levels)

D D v D vF

• Dutton Empirical Index

F orDt Dt Dt p

21.25 0.25 10.5H VDutton S S • Unbalanced flow (Koch et al., McCann, Knox et al.)

• Deformation X shear (Ellrod)

2 2 ( , )R J u v f u

H V

Deformation X shear (Ellrod)

1/22 2,V SH STDEF D D

v u u vD D

I DEF S

• Eddy dissipation rate (ε1/3) computed from second order structure functions of velocity and/or temperature

SH STD Dx y x y

2( ) [ ( ) ( )]qD s q x q x s

2/32/3 2/3( ) ( ( ) ( ) ) REFq q qs sD s C D s C s ( ) ( ( )( ) )REFq q q

But what are we forecasting?• “Aircraft scale” eddies that affect aircraft• Aircraft response is aircraft dependent but this is

what pilot reports: “light” “moderate” “severe”what pilot reports: “light”, “moderate”, “severe”• CANNOT forecast these levels for every aircraft in

the airspacePIREP• Instead need atmospheric turbulence measure (i.e.

aircraft independent measure)– We forecast EDR (= ε1/3 m 2/3s-1 ) EDR

PIREP

We forecast EDR ( ε m s ) • ICAO standard• Can relate to airborne and remote EDR

estimates

EDR

estimates• Can relate EDR to aircraft loads (σg ~ ε1/3 )• Convenient scale 0-1

F f ICAO t d d th h ld– For reference ICAO standard thresholds (2001,2010 ) for mid-sized aircraft are

• EDR=0.10, 0.3, 0.5 for “light”, “moderate”, “severe”, resp.

5• EDR=0.10, 0.4, 0.7 for “light”, “moderate”, “severe”, resp.

Conversion of diagnostics to EDR

• Each D is rescaled to an EDR assuming a log-

EDR=0.1,0.3,0.5

g gnormal distribution of edr

1/3log log ia b D • Where a and b are

chosen to give best fit to

g g i

gexpected lognormal distribution in the higherranges

• a and b depend on “Moderate”

1/3 1/3log logand SD

“Severe”

6• Which must be estimated from climatology DAL in situ EDR data

<logε1/3 >=‐2.85  SD[log ε1/3 ] = 0.57

Example diagnostics converted to EDR 3.5-hr forecast 3.3km conus grid*

7*courtesy J-H Kim, NASA Ames

Verification: PIREPs andIn situ EDR “measurements”

PIREPS

In situ EDR measurements• PIREPs

– Verbal subjectivej– Mean position error ~ 50 km

• Automated EDR (ε1/3 m2/3 / s) estimates– Calculation performed onboard ACMS, data

Red=severe, blue=MWT

p ,transferred via ACARS @ 8Hz

– Peak + mean over 1 min.– Position uncertainty < 10 km

• Currently deployed or planned to be deployed on– ~ 70 UAL B757-200s 24 hrs of UAL insitu– ~ 90 DAL B737-800s,737-700s– ~ 100 DAL B767-300ERs and -400ERs– ~ 400 SWA B737-800s,-700s

24 hrs of UAL insitu

– Currently ~ 5000/hour– Compared to 400/day PIREPs

– Must use both so need to be able to convert from

8pirep to edr -> simple mapping developed

24 hrs of DAL insitu

03 Jan 2010

Current GTG3 RUC-based performance(6-hr fcst ROC curves 12 mos. valid18Z) -

Bi di i i ti f f th d tBinary discrimination performance of smooth vs. moderate-or-greater (MOG) PIREPS + in situ edr data

GTG3Gct

ing

MO

G

UBF, LHFK

EDREllrod1

Null-MOG GTG3 AUC=0.812

SGS TKEof p

redi

c

Individual diagnostics

SGS TKE

obab

ility

P

ro

P b bilit f f l l

9High threshold Low threshold

Probability of false alarms

(Predict no turb) (Predict turb everywhere)

Other diagnostic combination strategies*

2010-2011 Cross-Validation ROC Curves2010-2011 Cross-Validation ROC Curves6-hr forecast 13km WRFRAPverified against insitu edr onlyEdr >0 3 vs edr < 0 3Edr >0.3 vs edr < 0.3FL>200Random forest AUC: 0.85KNN AUC:0.83Logistic Regression AUC: 0.80Current GTG AUC: 0.78/0.79

*Courtesy John Williams

Global GTGGFS

Global GTG

UKMET

ECMWFGTG output based on 3 global models for the same p g

case.  Contours are EDR (m2/3 s‐1) at FL29031 Dec 2011 6‐hr fcst valid 18Z

All models used native vertical gridsAll models used same number of diagnosticsAll models used same number of diagnostics

All models used same thresholds

Characterization studies• Use research simulation models to recreate both the

large scale forcing and smaller scale “turbulence” tolarge scale forcing and smaller scale turbulence to determine origins of recorded turbulence encounters

• Can control simulation options to isolate effectsCan control simulation options to isolate effects– Cloud/latent heating, – Cloud top radiative cooling

• Typically use multi-nested approach

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Simulations to identify relation of cirrus bands to turbulence*bands to turbulence*

*Knox et al. “Transverse cirrus bands in weather systems: a grand tour of an enduring enigma” Weather 2006

Example cirrus bands/turbulence 15 Nov 2011*

FL310

Forecasted turbulent areas

*Courtesy MelissaThomas DAL

Simulations of two cirrus bands cases

17 June 2005 Moderate and severe turbulence insitu edr measurements near Transverse (Radial) MCS Outflow Bands over central US

9 Sep 2010 : Moderate and severe turbulence reported in vicinity of mid-latitude cyclone over western

15

over central US- Trier & Sharman MWR 2009- Trier et al. JAS 2010

Pacific Ocean- Kim et al. submitted to MWR

Example 1. Out-of-cloud CIT near MCS*Observedturbulence

In situ edr + wind vectors

43 N

Note bands are transverse to wind vectors

43 N0905 UTC 16 June

40 N Model TKE

37 N

Courtesy UW CIMSS

*Trier and Sharman, MWR, 2009*Trier et al JAS 2010

ARWRF simulation ∆=3 kmCourtesy UW CIMSS

MCS CIT mechanism

With cloudNo cloud

No cloudWith cloud

• Strong vertical shear at flight levels due almost entirely to MCS outflow on north side• Vertical shear at flight levels on south side weaker because easterly outflow winds and shear are opposed by their westerly environmental (adiabatic) counterparts

Observations and WRF-Simulations of the17 June 2005 MCS Case 600m nest

Simulated Cloud Top Temperature (0950 UTC, t = 5.8 h ) Observed Turbulence @ 37 Kft (0936-0957 UTC) L=light M=ModerateIR Satellite at 0950 UTC 17 June 2005

17 June 2005 MCS Case – 600m nest

Observed Turbulence @ 37 Kft (0936-0957 UTC) L=light, M=ModerateIR Satellite at 0950 UTC 17 June 2005

Turbulence

UA776

Trier and Sharman (2009, Mon. Wea. Rev.) Observations and MCS-Scale SimulationsTrier et al. (2010, J. Atmos. Sci.) High-Resolution Simulation and Analysis of Radial Bands

m

4-hour Loop of Brightness Temperature, 12-km Moist Static Instability N < 0and 11.5-13-km Vertical Shear from 07-11 UTC 17 June with Dt = 10 min

2

Nor

th

Tb

East

• Anvil bands originate within zones of moist static instability• Bands are aligned along the anvil vertical shear vectorg g

Similar to horizontal convective rolls in boundary layer arising from thermal instability

Case 2: cirrus bands in mid-l tit d l 9 S 2010*

Control (CTL) Simulation No Cloud Radiative Feedback (NCR) Simulation

latitude cyclone 9 Sep 2010*Control (CTL) Simulation No Cloud Radiative Feedback (NCR) Simulation

xx

xx

x

Approximate Locations

x

Approximate Locations Approximate Locationsof Observed Turbulence

Approximate Locationsof Observed Turbulence

Domain 3= 3.3 km

Domain 3= 3.3 km

*J-H Kim submitted to MWR

Model-Derived Reflectivity and Sea-Level Pressure at 0300 UTC 9 Sep (t = 9 h)

500 kmJ-H Kim, submitted to MWR

Relation of wind shear vectors and

CTL at 2140 UTC 8 Sep 2010 (t = 3.67 h) CTL at 2340 UTC 8 Sep 2010 (t = 5.67 h)

stability to bands

Brightness Temperature, 11.75 km Moist Static Stability,200 kmBrightness Temperature, 11.75 km Moist Static Stability,10.75-12.75 km Wind Shear vectors

200 km

Brightness Temperature, and 11.75 km MSL Moist Static StabilityControl (CTL) Run No Cloud-Radiative Feedback (NCR) Run( ) ( )

2220 UTC 8 Sep(t = 4.33 h)

2310 UTC 8 Sep2310 UTC 8 Sep(t = 5.17 h)

200 km

Simulation of bands - summary

• Both cases show bands owe their existence to convective instability in anvil within background shear– MCS case: background shear mainly from upper-level

outflow– Pacific mid-latitude cyclone case: shear mainly from

j t tjet stream• Both have morphology akin to PBL rolls

Si b d i i t f ti l t bl i• Since bands originate from convectively unstable regions of anvil, it is not surprising that those areas are turbulentMCS case cloud top radiative cooling encourages• MCS case cloud-top radiative cooling encourages bands, in Pacific case it is crucial for bands

i23

Summary and future work• GTG

– Using an ensemble of turbulence diagnostics (GTG) instead of one diagnostic gives more robust performance

– Provides EDRTechnique can be used with any input NWP model– Technique can be used with any input NWP model

– Longer term goals• Forecast convective turbulence (CIT)• Provide probabilistic forecasts

– Use of in situ edr provides valuable verification source

• Numerical simulations– Use multi-nested approach to relate large scale to small scalesUse multi nested approach to relate large scale to small scales

for a variety of environments and turbulence sources– Helps understand turbulence genesis which may ultimately lead

to form lation of better diagnosticsto formulation of better diagnostics24