lower atmosphere middle atmosphere Gravity-Wave Drag Parameterization in a High-Altitude Prototype Global Numerical Weather Prediction System NRLDC

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1 Gravity-Wave Drag Parameterization in a High-Altitude Prototype Global Numerical Weather Prediction System NRLDC lower atmosphere Marine Meteorology Division (Code 7500) N. Baker T. Hogan M. Peng C. Reynolds B. Ruston L. Coy S. Eckermann A. Kochenash J. McCormack F. Sassi D. Allen K. Hoppel D. Kuhl G. Nedoluha Space Science Division (Code 7600) Remote Sensing Division (Code 7200) middle atmosphere Slide 1

2 NOGAPS-ALPHA Global Spectral Forecast Model 6-hourly update cycle 0-10 Day Forecasts 0-9 Hour Forecasts Global km observations over next 0-6 hours X a X b 6 hourly global km analysis fields Data Assimilation System NAVDAS/NAVDAS-AR y Slide 2

3 NOGAPS-ALPHA Navy Operational Global Atmospheric Prediction System (DoD NWP System) Advanced Level Physics & High Altitude NWP Prototype NAVDAS: NRL Atmospheric Variational Data Assimilation System (3DVAR) Top Data Insertion T79L hpa Top Data Insertion Operationally ~1 hpa Slide 3

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 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, Highaltitude data assimilation system experiments for the northern summer mesosphere season of 2007, J. Atmos. Sol.-Terr. Phys., 71, , 2009 Slide 4

5 Gravity Wave Drag (GWD) Parameterization Issues The GWD schemes must be tuned to improve both the forecasts x b and the analyses x a (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 x a provide an observations-based method of objectively tuning the GWD parameterizations? Slide 5

6 Multiwave Deterministic Nonorographic Gravity Wave Drag Parameterization 3. Add up flux deposition (drag) wave-by-wave to compute the total mean-flow acceleration 2. Discretize τ(c) among n gw =2n c +1=65 individual gravity waves & propagate each wave upwards 1. Source Level Momentum Flux τ(c) Slide 6

7 New Stochastic Version of the Nonorographic Gravity Wave Drag Scheme Deterministic source discretized identically in every grid box using n gw =65 different phase-speed waves Stochastic Analogue sample the source spectrum stochastically using n sgw =1 wave per grid box based on uniform random number generator New scheme is explicitly stochastic and intermittent In theory, 65 times faster! Slide 7

8 Single Column Tests Same time-mean drag (and diffusion and heating rates), but accompanied by explicit stochastic variability about the time mean Slide 8

9 Tests in NOGAPS-ALPHA: 3 Year Nature Run Zonal-Mean Zonal Winds for July Slide 9

10 Tests in NOGAPS-ALPHA: 3 Year Nature Run Zonal-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 times faster computationally in the model. [Eckermann, S. D., Explicitly stochastic parameterization of nonorographic gravity-wave drag, J. Atmos. Sci., in press, 2011] Slide 10

11 Tests in NOGAPS-ALPHA: 3 Year Nature Run Mean Spectral Variability for June Slide 11

12 Extension to Autoregressive (AR) Stochastic Model 1. Normal rather than uniform distribution of random phase speeds 2. 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) Existing Stochastic Gaussian flux spectrum Uniform random c s AR Stochastic Uniform flux spectrum Normal random c s Momentum Flux Phase Speeds c Slide 12

13 Gravity Wave Drag (GWD) Parameterization Issues The GWD schemes must be tuned to improve both the forecasts x b and the analyses x a (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 x a provide an observations-based method of objectively tuning the GWD parameterizations? Slide 13

14 Drag in the Analyses xa How Much from Observations, and How Much from Model Parameterizations? xa Slide 14

15 Mesospheric Temperature Corrections (x a -x b ) 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 15

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 Slide 16