Boundary-Layer Meteorology

, Volume 110, Issue 3, pp 405–433

Boundary-Layer Entrainment Estimation Through Assimilation of Radiosonde and Micrometeorological Data into a Mixed-Layer Model

  • Steven A. Margulis
  • Dara Entekhabi

DOI: 10.1023/B:BOUN.0000007221.53446.46

Cite this article as:
Margulis, S.A. & Entekhabi, D. Boundary-Layer Meteorology (2004) 110: 405. doi:10.1023/B:BOUN.0000007221.53446.46


In this study we estimate boundary-layer growth and entrainment bycombining radiosonde and micrometeorological observations with asimple coupled boundary-layer and land surface model. A variational(smoothing) approach is used to find the optimal estimate ofentrainment over the daytime window. This method is appealingbecause it accounts for the uncertainty in the various data streams,while enforcing the dynamics of the model (i.e., water and energybudgets in the boundary layer, mixed-layer growth, etc.). Thetraditional variational framework was modified in this study toinclude an ensemble approach, which not only yields a (mean) estimateof entrainment, but a measure of its uncertainty as well. Themethodology is applied to a field experiment site in Kansas. Resultsfrom this study indicate a much larger ratio of entrainment to surfacefluxes compared to early literature values from other sites. However,our results are consistent with recent estimates at the site usingindependent estimation methods. In tests where radiosonde data werewithheld, reasonable skill in entrainment estimation was still shown,suggesting the potential for more widespread applications where onlymicrometeorological data are available. Finally, the data assimilationframework presented here has not traditionally been used inatmospheric boundary-layer studies, and may provide a useful approachfor studying other aspects of the boundary layer in the future.

Data assimilation Entrainment Mixed-layer model 

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Steven A. Margulis
    • 1
  • Dara Entekhabi
    • 2
  1. 1.Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesU.S.A
  2. 2.Department of Civil and Environmental Engineering, Ralph M. Parsons LabMassachusetts Institute of TechnologyU.S.A

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