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  • Author or Editor: Michael P. Meyers x
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Temple R. Lee
Michael Buban
, and
Tilden P. Meyers


Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Ri b ) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Ri b parameterizations to stable regimes and developed parameterizations for the friction velocity (u *), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Ri b parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Ri b parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Ri b parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.

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