Slide 1
Gravity-Wave Drag Parameterization in a High-Altitude Prototype Global Numerical Weather Prediction System
Marine Meteorology Division (Code 7500)
NRLDC
lower atmosphere
middle atmosphere
Remote Sensing Division (Code 7200)
Space Science Division (Code 7600)
N. BakerT. HoganM. PengC. ReynoldsB. Ruston …
L. CoyS. EckermannA. KochenashJ. McCormackF. Sassi
D. AllenK. HoppelD. KuhlG. Nedoluha
Slide 2
Global SpectralForecast Model
Data Assimilation SystemNAVDAS/NAVDAS-AR
Global 0-100 km observationsover next 0-6 hours
Xb
0-10 Day Forecasts
0-9 Hour Forecasts
y
Xa
6 hourly global0-100 km analysis fields
NOGAPS-ALPHAhttp://uap-www.nrl.navy.mil/dynamics/html/nogaps.html
6-hourly update cycle
Slide 3
NOGAPS-ALPHA
~1 hPaTop Data Insertion
Operationally
Navy Operational Global Atmospheric Prediction System (DoD NWP System)
Advanced‐Level Physics & High Altitude NWP Prototype
0.002 hPaTop Data Insertion
NAVDAS: NRL Atmospheric Variational Data Assimilation System (3DVAR)
T79L68
Slide 4
Gravity-Wave Drag Parameterizations
• Orographic gravity-wave drag (OGWD) schemes– Lindzen type scheme (Palmer et al. 1986)– New Met Office scheme (Webster et al. 2003)– Developmental NRL scheme (Kim and Doyle 2005)
• Nonorographic gravity-wave drag (NGWD) schemes– Rayleigh friction– Alexander and Dunkerton (1999) multiwave scheme– Kim-Chun convective GWD schemes– Hines (1991) Doppler-spread scheme– WACCM 3.0 65-wave Lindzen scheme (Garcia et al. 2007)
NOGAPS-ALPHA T79L68 “Frozen” Production Configuration (Eckermann et al. JASTP 2009)
Eckermann, S. D., K. W. Hoppel, L. Coy, J. P. McCormack, D. E. Siskind, K. Nielsen, A. Kochenash, M. H. Stevens, C. R. Englert, and M. Hervig, High-altitude data assimilation system experiments for the northern summer mesosphere season of 2007, J. Atmos. Sol.-Terr. Phys., 71, 531-551, 2009
Slide 5
Gravity Wave Drag (GWD) Parameterization Issues…The GWD schemes must be tuned to improve both the forecasts xb
and the analyses xa (to eliminate obs-forecast [O-F] biases)
1. Computational Speed• WACCM 3.0 NGWD schemes consumes ~20-40% of the total run time of the
forecast model
• To be competitive for transition to operational NWP centers, this is (at least) an order of magnitude too expensive
Question 1: how can these schemes be made much more efficient?
2. Extensive (endless?) tuning • many forecast model runs and iterative tuning are needed to reproduce temperature
fields from SABER and MLS and long-term climatologies
• essential to reduce biases between observations and background (O-Fs) that affect the quality of the final analysis (A) fields
Question 2: Can the analysis fields xa provide an observations-based method of objectively tuning the GWD parameterizations?
Slide 6
Multiwave Deterministic Nonorographic Gravity Wave Drag Parameterization
1. Source Level Momentum Flux τ(c)
2. Discretize τ(c) among ngw=2nc+1=65 individual gravity waves & propagate each wave upwards
3. Add up flux deposition (drag) wave-by-wave to compute the total mean-flow acceleration
Slide 7
New Stochastic Version of the Nonorographic Gravity Wave Drag Scheme
Deterministic source• discretized identically in every
grid box using ngw=65 different phase-speed waves
Stochastic Analogue• sample the source spectrum
stochastically using nsgw=1 wave per grid box based on uniform random number generator
• New scheme is explicitlystochastic and intermittent
• In theory, 65 times faster!
Slide 8
Single Column Tests
• Same time-mean drag (and diffusion and heating rates), but accompanied by explicit stochastic variability about the time mean
Slide 10
Tests in NOGAPS-ALPHA: 3 Year Nature RunZonal-Mean GW-induced Acceleration and Heating Rates for July
Stochastic 1-wave scheme produces identical climate to deterministic 65-wave scheme but it is between 10-20 times faster computationally in the model.
[Eckermann, S. D., Explicitly stochastic parameterization of nonorographic gravity-wave drag, J. Atmos. Sci., in press, 2011]
Slide 12
Extension to Autoregressive (AR) Stochastic Model
Existing StochasticGaussian flux spectrum
Uniform random c’s
Momentum Flux
Phase Speeds c
AR StochasticUniform flux spectrum
Normal random c’s
1. Normal rather than uniform distribution of random phase speeds2. Phase speed now has a “time history” (temporal autocorrelation)3. Autocorrelation values (0<AC<1) control degree of temporal scale of
the random walk (AC 0.0 short, AC 1.0 long)
Slide 13
Gravity Wave Drag (GWD) Parameterization Issues…The GWD schemes must be tuned to improve both the forecasts xb
and the analyses xa (to eliminate obs-forecast [O-F] biases)
1. Computational Speed• WACCM 3.0 NGWD schemes consumes ~20-40% of the total run time of the
forecast model
• To be competitive for transition to operational NWP centers, this is (at least) an order of magnitude too expensive
Question 1: how can these schemes be made more efficient?
2. Extensive (endless?) tuning • many forecast model runs and iterative tuning are needed to reproduce temperature
fields from SABER and MLS and long-term climatologies
• essential to reduce biases between observations and background (O-Fs) that affect the quality of the final analysis (A) fields
Question 2: Can the analysis fields xa provide an observations-based method of objectively tuning the GWD parameterizations?
Slide 14
Drag in the Analyses xa – How Much from Observations, and How Much from Model Parameterizations?
xa
Slide 15
Mesospheric Temperature Corrections (xa-xb) from SABER and MLS for one 6-hour cycle
Preliminary Hypothesis:• Sparse data
assimilated in upper mesosphere constrain only zonal wavenumbers ~1-8
• Parameterized OGWD has large signal in analyses since it is applied at zonal wavenumbers>8
• Parameterized NWD applied gravest zonal wavenumbers, so can be corrected by observations
Slide 16
Summary
GWD parameterizations presents somewhat different (but similarly thorny) challenges for operational NWP systems (compared to climate models)
• must be very cheap computationally (cheaper than climate model requirements
• have important complex influences that manifest both in the forecasts and in the analyses
A new explicitly stochastic analogue of an existing NGWD scheme yields
• An order of magnitude improvement in computational speed
• Essentially identical long-term climate in nature runs
• No excessive increases in variability at smallest space-time GCM scales
• Can add greater and more realistic spread for ensemble forecasting applications
Subgridscale zonal drag residuals in the analyses
• Show signals of parameterized OGWD from forecast model
• Suggests NGWD rather than OGWD might be more effectively constrained by sparse data assimilated in mesosphere