Evaluation of Monin–Obukhov and Bulk Richardson Parameterizations for Surface–Atmosphere Exchange
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Evaluation of Monin–Obukhov and Bulk Richardson Parameterizations for Surface–Atmosphere Exchange
  • Published Date:

    2020

  • Source:
    J. Appl. Meteor. Climatol. (2020) 59 (6): 1091–1107.
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  • Description:
    The Land–Atmosphere Feedback Experiment (LAFE) was a field campaign to investigate influences of different land surface types on the atmospheric boundary layer (ABL). The primary goals of LAFE were to better understand ABL development and structure and to improve turbulence parameterizations in numerical weather prediction models. Three 10-m micrometeorological towers were installed over different land surface types (i.e., early growth soybean, native grassland, and mature soybean) along a 1.7-km southwest–northeast-oriented line. All towers measured standard meteorological variables in addition to heat, moisture, and momentum fluxes. In this study, we used these measurements to evaluate the validity of applying Monin–Obukhov similarity theory (MOST) to represent surface–atmosphere exchange over different land surface types. We investigated relationships between stability length ζ and the dimensionless wind shear ϕm, temperature gradient ϕh, and moisture gradient ϕq as well as relationships between bulk Richardson number Rib, friction coefficient Cu, heat-transfer coefficient Ct, and moisture-transfer coefficient Cr. We evaluated the new similarity functions developed using independent datasets obtained during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). We found that using the Rib functions rather than the more traditional ζ functions to compute wind, temperature, and moisture yielded better agreement with the VORTEX-SE observations. These findings underscore limitations in MOST and motivate the need to consider modifying the functional forms of the similarity equations that form the basis for surface-layer parameterizations in numerical weather prediction models.
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