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MODEL SENSITIVITY TO SPACE- AND IN SITU- BASED SUB-GRID FLUX PARAMETERIZATION

MODEL SENSITIVITY TO SPACE- AND IN SITU- BASED SUB-GRID FLUX PARAMETERIZATION. Gad Levy NorthWest Research Associates and Jordan C. Alpert NCEP/Environmental Modeling Center http://www.nwra.com/resumes/levy/papers/ January 13,2005: 85thAMS Meeting. Motivation & Background.

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MODEL SENSITIVITY TO SPACE- AND IN SITU- BASED SUB-GRID FLUX PARAMETERIZATION

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  1. MODEL SENSITIVITY TO SPACE- AND IN SITU- BASED SUB-GRID FLUX PARAMETERIZATION Gad Levy NorthWest Research Associates and Jordan C. Alpert NCEP/Environmental Modeling Center http://www.nwra.com/resumes/levy/papers/ January 13,2005: 85thAMS Meeting

  2. Motivation & Background • Significant uncertainty in model fluxes due to unresolved subgrid variability. • Some variability is consistent and can be parameterized based on observations. • Large-scale atmospheric models surface fluxes are sensitive to unresolved (subgrid) variability in the wind (bulk formulas: function of the surface winds). • Advances in observing systems allow parameterizing subgrid flux due to wind directional variability.

  3. Test the impact of a subgrid parameterization (Levy and Vickers, 1999 and Levy, 2000) on the NCEP operational forecasting model. Sensitivity tests: Control Parameterization applied with land mask (ASF1) Parameterization applied globally (ASF2) Objective and Method

  4. The Parameterization1 • Accounts for subgrid variability due to directional variability. • Formulated as a velocity scale term added to current model bulk formulation. • Formulated as a function of model grid scale. • Based on a best fit of collocated scatterometers (2) and buoy data. 1Levy & Vickers, 1999; Levy, 2000 U = (V2 + Vsg2)1/2 Vsg =(X/D-1)

  5. The Parameterization (cont’d) • Some seasonal and regional variability is evident in data used • Flux enhancement) is largest for low winds & for larger grids. • Satellite (ERS, NSCAT, Qscat) /buoy results consistent with aircraft results (including over land and equatorial)

  6. Effect of Parameterization on Fluxes Estimated based on climatology, Quickscat, and assumed 250 km grid Latent Heat Sensible Heat Momentum

  7. NCEP global forecasts system (GFS) model used Spectral model using a 64 level sigma vertical coordinate system Triangular truncation of T254 with a physical Gaussian grid of 768x384 Existing model flux parameterization already include stratification effects; low-wind minimum Model run globally Mean values for Vsg used (regional variation neglected) Vsg likely underestimated over land Only one case (5 days forecast with initial conditions from 8 February 2004 ) run Impact of momentum flux enhancement hard to assess in non-coupled model Focus on W. Hemisphere Model; Experimental Design; Limitations

  8. The Synoptic Scenario (1000 mb)

  9. Latent Heat Flux Evolution (ASF2)

  10. Precipitation Systems Evolution

  11. Impact on Heat Fluxes • All significant impacts limited to just a few locations • Significant (20%-80%) impact in LHF in convective (tropical) systems shows early; reflected in convective precipitation • Impact in LHF at mid/high latitudes shows later; limited to frontal systems; reflected in geopotential and (total) precipitation redistribution • Significant impact in sensible heat flux limited (frontal & land)

  12. Impact on precipitation

  13. Summary and Conclusions • Parameterization has significant direct (flux enhancement) and indirect (convective and total precipitation, synoptic wave amplification) impacts on GFS forecasts • Significant impacts are not evenly distributed • Impacts on middle and higher latitude systems: • amplify the synoptic waves and change their propagation • redistribute stratiform and convective precipitation • impacts develop (noted) later (at 96 hrs. or 120 hrs.) • Impacts on equatorial and tropical systems: • enhance latent heat fluxes • enhance tropical convective precipitation • Impacts develop early (at 24 hrs.)

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