FAA AWRP-sponsored Turbulence Nowcasts/Forecasts

Tri-Agency Review2 Dec 2010

Robert SharmanNCAR/RAL

Boulder, CO USAsharman@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.)

FAA turbulence products aimed at IOC

• In situ edr data– EDR = ε1/3 (m 2/3s-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

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 = W1D1* + W2D2* + W3D3* + ….

04/21/234

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

04/21/235

GTG Prob > light

Prob > mod Prob > severe

Use of indices as ensembles provides confidence values (or uncalibrated probabilities)

0 h forecast valid at 22 Sep 2006 15Z

Red=.75

Red=.30Red=.30

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|, CT

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 domainLower resolution

Conversion of diagnostics (D) to D*(ε1/3)

7

• Assume turbulence in the UTLS has a log normal distribution of ε1/3

• Consistent with GASP, research aircraft, and NWP data

• So rescale diagnostic D to ε1/3 through

• Where a and b are chosen to give best fit to expected lognormal distribution in the higher ranges

1/3log log ia b D

PACDEX data

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

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 (= ε1/3 m 2/3s-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

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

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

Idealized single sounding – animation of w at z=12 km*

12

*Courtesy Rob Fovell UCLA

4 km w/YSU/LFO 4 km w/QNSE/Seifert

1.5 km w/YSU/Seifert 1.5 km w/YSU/LFO

More sensitivity studies

GTGN Example: Components & Output: 20100813 at 22z FL380

GTG2 1hr Fcst

GTGN & Next 15min In situIn situ, Pireps (1 hr prior) & NTDA

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 EDRdBZ

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