THE OPERATIONAL PREDICTION OF MOUNTAIN WAVE TURBULENCE (MWT) USING A HIGH RESOLUTION NONHYDROSTATIC...

THE OPERATIONAL PREDICTION OF MOUNTAIN WAVE TURBULENCE (MWT)

USING A HIGH RESOLUTION NONHYDROSTATIC MESOSCALE MODEL

Bob Sharman, Bill Hall, Rod Frehlich, Teddie Keller

sharman@ucar.edu

World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting

Toulouse, FRANCE 7, Sep 2005

MWT forecasting – “traditional” approach

• Identify MWT-prone areas• Use MWT “diagnostics”

– Empirical ‘rules-of-thumb”– linear theory

• Disadvantages– NWP model is hydrostatic– Waves are nonhydrostatic– Waves and wave “breaking”

may be very small scale– Nonlinear effects important

• Nonlinear forcing at lower boundary

• Wave-wave interactions• Wave-induced critical

levels

Terra MODIS image 04/04/200406 :30 UTC over Crozet Islands,Indian Ocean

MWT forecasting – another approach

• Mesoscale models have proven capability to model mountain waves (and turbulence?)

• Could set up mesoscale model grids over MWT-prone areas

• Required resolution ~ 1-3 km horizontally

• Uses NWP model for BCs and ICs

• Produces 1-6 hr forecasts

Nested high resolution grid

Outer NWP domain

Contours of average annual counts of MOG MWT PIREPs in 40 km2 areas over CONUS based on 10 years worth of PIREPS

Case studies

• Assess feasibility of using multi-nested model for forecasting MWT– Ability of model to reproduce observed

waves and turbulence– Assess timing requirements for

operational use• Used two nonhydrostatic models

– Clark-Hall anelastic model– AR WRF

a)

b)

c)

Flight recorder data showing various aircraft parameters as a function of time. Turbulence incident began about 8.47 UTC. Panels show a) wind speed m/s ,b) wind direction (degrees) and c) acceleration in g’s.

Example 1: Severe MWT over Alamosa CO 27 Feb 2004

Water vapor image from MODIS satellite, Feb. 27, 2004 at 5:25 UTC.

Domain 1

Domain 3

Terrain contours for the outer domain (domain 1) and inner most domain (domain 3). Red and blue brackets delineate position of domains 2 and 3. Aircraft flight track is represented by red line in domain 3.

Example 1: Clark-Hall model setup and results

Example 2: Widespread MWT over CO 6 Mar 2004

Water vapor image from MODIS satellite, Mar 6, 2004 at 19:50 UTC.

Example 2: WRF ARW Model setup

• 24-hr forecast from 0Z

• 4 nested domains: 27-9-3-1 km resolution 348x348 @ 1km

• 60 vertical levels, avg spacing at 10 km is about 600 m

Example 2 results

41 N

37 N

Example 2 results (cont.)

upper boundarycondition

Example 2 results (cont.) – edr diagnosis of turbulence (resolved)

Timing

Model description Δx (km) Δt (sec) nx ny nz Timing (hr)RUC 20 30 301 225 50 0.38

Clark-Hall 12 45 122 122 45 8.57Alamosa case 4 15 122 122 72 1 5 194 194 72

Clark-Hall 18 60 130 194 45 8.966 Mar case 6 20 194 290 74 3 10 194 290 74

WRF 27 120 84 128 61 8.006 Mar case 9 40 193 193 61 3 13.3 193 289 61

• Model configurations and timings used in the case studies. The timing is based on one hour of model time using a single 1.3 GHz processor CPU

• 32 processors will easily allow operationally useful MWT forecasts

Conclusions

• MW or lee waves are fairly well understood theoretically

• In practice, many facets lead to a “gravity wave soup” that make theory difficult to apply, especially to “breaking”

• High resolution simulations seem to reproduce the main features of the waves and “turbulence”

• EDR diagnostics capture MWT fairly well for both coarse and high resolution models

• Sensitivity studies to model resolutions ongoing• WRF model looks promising once upper boundary

condition is fixed

Contours of average annual counts of MOG MWT PIREPs in 40 km2 areas over CONUS based on 10 years worth of PIREPS

Where is MWT?

edr diagnostic

• Eddy dissipation rate or σw

(Frehlich and Sharman 2004)

2 2 / 3

2 / 3 21 1

1 14 / 3

1LLcor 4 / 3 2 / 3

1 2 1

2 / 3

( ) ( ) ( ) ( ) ( )

( ) ln (derived fromGASPdata)

( / )( )

1 ( / ) ( / )

( )averaged over severals

( ) ( )

q K cor ref

LLref

q

K cor ref

D s q x q x s C D s D s

b cD s s s s

a as p

D ss p p s p

D sC D s D s

Expected +2/3 slope

Model shape

Model deficits

Example 2 results (cont.)

37 N

41 N