THE OPERATIONAL PREDICTION OF MOUNTAIN WAVE TURBULENCE (MWT) USING A HIGH RESOLUTION NONHYDROSTATIC MESOSCALE MODEL Bob Sharman, Bill Hall, Rod Frehlich, Teddie Keller 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/ :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.

Dom ain 1 Domai n 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: km resolution 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 boundary condition

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

Timing Model description Δx (km) Δt (sec) nx ny nz Timing (hr) RUC Clark-Hall Alamosa case Clark-Hall Mar case WRF Mar case 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 km 2 areas over CONUS based on 10 years worth of PIREPS Where is MWT?

edr diagnostic Eddy dissipation rate or σ w (Frehlich and Sharman 2004) Expected +2/3 slope Model shape Model deficits

Example 2 results (cont.) 37 N 41 N