Spatial and Temporal Scales of Boundary Layer Wind Predictability in Response to Small-Amplitude Land Surface Uncertainty

Joshua P. Hacker National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Joshua P. Hacker in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Predictability experiments with the Weather Research and Forecast (WRF) model as a proxy for the atmosphere are analyzed to quantify the spatial and temporal scales of boundary layer wind response to land surface perturbations. Soil moisture is chosen as the land surface variable subject to uncertainty because the atmosphere is known to be sensitive to its state. A range of experiments with spatially correlated, small-amplitude perturbations to soil moisture leads to results that show the dependence of predictability on atmospheric conditions. The primary conclusions are as follows: 1) atmospheric conditions, including static instability and the presence of deep convection, determine whether large errors and local loss of predictability are possible in response to soil moisture errors; 2) the scale of soil moisture uncertainty determines scales of PBL wind predictability when the atmosphere is resistant to upscale error transfer, but when the atmosphere is sensitive the scale and magnitude of soil moisture uncertainty are not important after a few hours; and 3) nonlinear error growth is present whether or not the atmosphere is relatively sensitive to soil moisture uncertainty, leading to doubling times of minutes to hours for finite-sized perturbations. Similar results could be expected from other land surface variables or parameters that exert time-dependent forcing on the atmosphere that is similar in magnitude and scale to that of soil moisture.

* Current affiliation: Department of Meteorology, Naval Postgraduate School, Monterey, California.

Corresponding author address: Joshua Hacker, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. Email: hacker@ucar.edu

Abstract

Predictability experiments with the Weather Research and Forecast (WRF) model as a proxy for the atmosphere are analyzed to quantify the spatial and temporal scales of boundary layer wind response to land surface perturbations. Soil moisture is chosen as the land surface variable subject to uncertainty because the atmosphere is known to be sensitive to its state. A range of experiments with spatially correlated, small-amplitude perturbations to soil moisture leads to results that show the dependence of predictability on atmospheric conditions. The primary conclusions are as follows: 1) atmospheric conditions, including static instability and the presence of deep convection, determine whether large errors and local loss of predictability are possible in response to soil moisture errors; 2) the scale of soil moisture uncertainty determines scales of PBL wind predictability when the atmosphere is resistant to upscale error transfer, but when the atmosphere is sensitive the scale and magnitude of soil moisture uncertainty are not important after a few hours; and 3) nonlinear error growth is present whether or not the atmosphere is relatively sensitive to soil moisture uncertainty, leading to doubling times of minutes to hours for finite-sized perturbations. Similar results could be expected from other land surface variables or parameters that exert time-dependent forcing on the atmosphere that is similar in magnitude and scale to that of soil moisture.

* Current affiliation: Department of Meteorology, Naval Postgraduate School, Monterey, California.

Corresponding author address: Joshua Hacker, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. Email: hacker@ucar.edu

Save
  • Alapaty, K., S. Raman, and D. Niyogi, 1997: Uncertainty in the specification of surface characteristics: A study on prediction errors in the boundary layer. Bound.-Layer Meteor., 82 , 473–500.

    • Search Google Scholar
    • Export Citation
  • Aurell, E., G. Boffetta, A. Crisanti, G. Paladin, and A. Vulpiani, 1997: Predictability in the large: An extension of the concept of Lyapunov exponent. J. Phys., 30A , 1–26.

    • Search Google Scholar
    • Export Citation
  • Boffetta, G., A. Crisanti, F. Paparella, A. Provenzale, and A. Vulpiani, 1998: Slow and fast dynamics in coupled systems: A time series analysis view. Physica D, 116 , 301–312.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 2007: Description and evaluation of the characteristics of the NCAR high-resolution land data assimilation system. J. Appl. Meteor. Climatol., 46 , 694–713.

    • Search Google Scholar
    • Export Citation
  • Cheng, W. Y. Y., and W. R. Cotton, 2004: Sensitivity of a cloud-resolving simulation of the genesis of a mesoscale convective system to horizontal heterogeneities in soil moisture initialization. J. Hydrometeor., 5 , 934–958.

    • Search Google Scholar
    • Export Citation
  • Daley, R., and T. Mayer, 1986: Estimates of global analysis error from the global weather experiment observational network. Mon. Wea. Rev., 114 , 1642–1653.

    • Search Google Scholar
    • Export Citation
  • Drusch, M., and P. Viterbo, 2007: Assimilation of screen-level variables in ECMWF’s Integrated Forecast System: A study on the impact of the forecast quality and analyzed soil moisture. Mon. Wea. Rev., 135 , 300–314.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 3077–3107.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., K. E. Mitchell, Y. Lin, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land-surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108D , 8851. doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Errico, R., 1985: Spectra computed from a limited-area grid. Mon. Wea. Rev., 113 , 1554–1562.

  • Errico, R., and D. Baumhefner, 1987: Predictability experiments using a high-resolution limited-area model. Mon. Wea. Rev., 115 , 488–504.

    • Search Google Scholar
    • Export Citation
  • Górska, M., J. V-G. de Arellano, M. A. LeMone, and C. C. van Heerwaarden, 2008: Mean and flux horizontal variability of virtual potential temperature, moisture, and carbon dioxide: Aircraft observations and LES study. Mon. Wea. Rev., 136 , 4435–4451.

    • Search Google Scholar
    • Export Citation
  • Holt, T. R., D. Noyogi, F. Chen, K. Manning, M. LeMone, and A. Qureshi, 2006: Effect of land–atmosphere interactions on the IHOP 24–25 May 2002 convection case. Mon. Wea. Rev., 134 , 113–133.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., and H-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124 , 2322–2339.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., J. Dudhia, and S-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132 , 103–120.

    • Search Google Scholar
    • Export Citation
  • Hong, S-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with explicit treatment of entrainment processes. Mon. Wea. Rev., 134 , 2318–2341.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43 , 170–181.

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47 , 2784–2802.

    • Search Google Scholar
    • Export Citation
  • Knievel, J. C., G. H. Bryand, and J. P. Hacker, 2007: Explicit numerical diffusion in the WRF model. Mon. Wea. Rev., 135 , 3808–3824.

    • Search Google Scholar
    • Export Citation
  • LeMone, M., M. Tewari, F. Chen, J. Alfieri, and D. Niyogi, 2008: Evaluation of the Noah land surface model using data from a fair-weather IHOP_2002 day with heterogeneous surface fluxes. Mon. Wea. Rev., 136 , 4915–4941.

    • Search Google Scholar
    • Export Citation
  • Mahfouf, J-F., E. Richard, and P. Mascart, 1987: The influence of soil and vegetation on the development of mesoscale circulations. J. Appl. Meteor., 26 , 1483–1495.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102D , 16663–16682.

    • Search Google Scholar
    • Export Citation
  • Mullen, S. L., and D. P. Baumhefner, 1989: The impact of initial condition uncertainty on numerical simulations of large-scale explosive cyclogenesis. Mon. Wea. Rev., 117 , 2800–2821.

    • Search Google Scholar
    • Export Citation
  • Niyogi, D. S., S. Raman, and K. Alapaty, 1999: Uncertainty in the specification of surface characteristics, Part II: Hierarchy of interaction-explicit statistical analyses. Bound.-Layer Meteor., 91 , 341–366.

    • Search Google Scholar
    • Export Citation
  • Noh, Y., W. G. Cheon, S. Y. Hong, and S. Raasch, 2003: Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107 , 401–427.

    • Search Google Scholar
    • Export Citation
  • Pielke Sr., R. A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39 , 151–177.

    • Search Google Scholar
    • Export Citation
  • Pinty, J-P., P. Mascart, E. Richard, and R. Rosset, 1989: An nvestigation of mesoscale flows induced by vegetation inhomogeneities using an evapotranspiration model calibrated against HAPEX-MOBILHY data. J. Appl. Meteor., 28 , 976–992.

    • Search Google Scholar
    • Export Citation
  • Rogers, E., D. Parrish, and G. DiMego, 1999: Changes to the NCEP operational Eta analysis. National Weather Service Tech. Proc. Bull. 454, 24 pp. [Available online at http://www.nws.noaa.gov/om/tpb/454.html].

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132 , 3019–3032.

  • Stull, R. B., 1998: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.

  • Tao, K., and A. P. Barros, 2008: Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP). J. Geophys. Res., 113 , D14125. doi:10.1029/2007JD009337.

    • Search Google Scholar
    • Export Citation
  • Tribbia, J. J., and D. P. Baumhefner, 1988: The reliability of improvements in deterministic short-range forecasts in the presence of initial state and modeling deficiencies. Mon. Wea. Rev., 116 , 2276–2288.

    • Search Google Scholar
    • Export Citation
  • Tribbia, J. J., and D. P. Baumhefner, 2004: Scale interactions and atmospheric predictability: An updated perspective. Mon. Wea. Rev., 132 , 703–713.

    • Search Google Scholar
    • Export Citation
  • Troen, I., and L. Mahrt, 1986: A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation. Bound.-Layer Meteor., 37 , 129–148.

    • Search Google Scholar
    • Export Citation
  • Weckworth, T., and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85 , 253–277.

    • Search Google Scholar
    • Export Citation
  • Xue, M., 2000: High-order monotonic numerical diffusion and smoothing. Mon. Wea. Rev., 128 , 2853–2864.

  • Zhang, D., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer—Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21 , 1594–1609.

    • Search Google Scholar
    • Export Citation
  • Zilitinkevich, S. S., 1995: Non-local turbulent transport: Pollution dispersion aspects of coherent structure of convective flows. Air Pollution Theory and Simulation, H. Power, N. Moussiopoulos, and C. A. Brebbia, Eds., Vol. I, Air Pollution III, Computational Mechanics Publications, 53–60.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 329 193 22
PDF Downloads 66 33 6