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NASA Applied Science Program Review Session 2B: Turbulence NCAR/RAL Boulder, CO USA. ASAP Turbulence Research at NCAR. Mountain Wave Turbulence. Bob Sharman & David Johnson NCAR. Background – known turbulence sources. Clear-air Turbulence (CAT). Cloud-induced or

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Nasa applied science program review session 2b turbulence ncar ral boulder co usa

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


Background known turbulence sources
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


Turbulence forecast product graphical turbulence guidance gtg
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


Gtg process
GTG process (GTG)

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


Mountain wave turbulence mwt
Mountain wave turbulence (MWT) (GTG)

  • 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


Turbulence climatology increased levels near mountains 15 years of pireps
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


Turbulence levels are significantly higher over mountainous terrain
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.



ASAP MWT forecasting goal/approach the Western U. S.

  • 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


Connection to nextgen 4d data cube

4D Weather the Western U. S.

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)


Mwt diagnostics
MWT diagnostics the Western U. S.

  • 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


Semi-empirical MWT forecasting approach the Western U. S.

  • 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!!


Mwt pireps climatology
MWT pireps climatology the Western U. S.

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)


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


CWEDR MWT PIREPs

+

Current experimental GTG2

Mountain Wave GTG3 is

Combination of GTG+ MWT diagnostic (CWEDR)

GTG3

CASE STUDY


CASE STUDY MWT PIREPsExample: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


Turbulence encounter MWT PIREPs (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


Another possible MOG discriminator: Wave pattern complexity? MWT PIREPs

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


Summary/Future work MWT PIREPs

  • 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!


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