New Parameterizations of Turbulence Statistics for the Atmospheric Surface Layer

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), upsilon (meridional), and w (vertical) wind components (i.e., sigma(u), sigma(upsilon), and sigma(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 (sigma(theta) and sigma(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 sigma(upsilon), sigma(w), sigma(theta), and sigma(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. Significance StatementDeficiencies in Monin-Obukhov similarity theory (MOST) are well known, yet MOST forms the basis in weather forecasting models for describing heat, moisture, and momentum transfer between the land surface and atmosphere. We expanded upon previous work suggesting a MOST alternative called the bulk-Richardson approach. We used data collected from meteorological towers installed in Oklahoma and compared the bulk-Richardson approach with MOST. We evaluated these two approaches using data from meteorological towers installed in Oklahoma and Alabama and found that, overall, the bulk-Richardson approach performed better than MOST in determining the 30-min variability in temperature, moisture, and wind. This result provides additional motivation to use a bulk-Richardson approach in weather forecasting models because doing so will likely yield improved forecasts.