New Parameterizations of Turbulence Statistics for the Atmospheric Surface Layer

• Published in: Monthly weather review 2023-01, Vol.151 (1), p.85-103
• Main Authors: Lee, Temple R., Meyers, Tilden P.
• Format: Article
• Language: English
• Subjects: Atmosphere; Atmospheric models; Atmospheric turbulence; Datasets; Enthalpy; Heat; Heat flux; Heat transfer; Kinetic energy; Latent heat; Latent heat flux; Low wind speeds; Mathematical models; Moisture; Moisture effects; Momentum; Momentum transfer; Performance evaluation; Sensible heat; Sensible heat flux; Sensible heat transfer; Similarity theory; Soybeans; Standard deviation; Statistical methods; Statistics; Surface boundary layer; Surface layers; Temperature; Towers; Turbulence; Turbulent kinetic energy; Weather forecasting; Wind; Wind speed
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/MWR-D-22-0071.1

Recent work has shown that bulk-Richardson (Ri b ) parameterizations for friction velocity, sensible heat flux, and latent heat flux have similar, and in some instances better, performance than long-standing parameterizations from Monin–Obukhov similarity theory (MOST). In this work, we expanded upon new Ri b parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), υ (meridional), and w (vertical) wind components (i.e., σ u , σ υ , and σ w , respectively), which allowed us to derive Ri b -based parameterizations of turbulent kinetic energy ( e ), and standard deviations in the 30-min temperature and moisture measurements ( σ θ and σ q , respectively). We used datasets from three 10-m micrometeorological towers installed during the Land Atmosphere Feedback Experiment (LAFE) conducted in Oklahoma from 1 to 31 August 2017 and evaluated the new parameterizations by comparing them against parameterizations from MOST. We used the LAFE datasets and fully independent datasets obtained from two micrometeorological towers installed in Alabama between February 2016 and April 2017 to evaluate the performance of the parameterizations. Based on the slope of the relationship between the observed and parameterized turbulence statistics ( m b ) and the coefficient of correlation ( r ), we found that the Ri b relationships generally performed better than MOST at parameterizing σ υ , σ w , σ θ , and σ q , and the Ri b relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Ri b parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Ri b -based parameterizations in weather forecasting models.