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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 (Rib) 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 Rib 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 Rib parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Rib 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 Rib 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. Significance Statement: Models used to forecast the weather represent physical processes occurring in Earth's atmosphere using complex mathematical relationships. Many weather models use an approach developed in the 1950s referred to as Monin–Obukhov similarity theory, or MOST, to determine how heat, moisture, and wind change vertically above the land surface. However, MOST limitations are well known within the scientific community. We implemented a method different from MOST called the bulk Richardson approach. We found the bulk Richardson approach generally yielded predictions of how heat, moisture, and wind change with height near the ground that were just as good as, and in some instances better than, predictions of these quantities obtained using MOST. This result motivates us to consider implementing the bulk Richardson approach into weather models, as doing so is expected to lead to their improvement. [ABSTRACT FROM AUTHOR]

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 z and the dimensionlesswind shear fm, temperature gradient fh, and moisture gradient fq 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 theOrigins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). We found that using the Rib functions rather than themore traditional z functions to computewind, temperature, andmoisture yielded better agreementwith 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. [ABSTRACT FROM AUTHOR]

Rotary-wing small unmanned aircraft systems (sUAS) are increasingly being used for sampling thermodynamic and chemical properties of the Earth's atmospheric boundary layer (ABL) because of their ability to measure at high spatial and temporal resolutions. Therefore, they have the potential to be used for long-term quasi-continuous monitoring of the ABL, which is critical for improving ABL parameterizations and improving numerical weather prediction (NWP) models through data assimilation. Before rotary-wing aircraft can be used for these purposes, however, their performance and the sensors used therein must be adequately characterized. In the present study, we describe recent calibration and validation procedures for thermodynamic sensors used on two rotary-wing aircraft: A DJI S-1000 and MD4-1000. These evaluations indicated a high level of confidence in the on-board measurements. We then used these measurements to characterize the spatiotemporal variability of near-surface (up to 300-m AGL) temperature and moisture fields as a component of two recent field campaigns: The Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast U.S. (VORTEX-SE) in Alabama, and the Land Atmosphere Feedback Experiment (LAFE) in northern Oklahoma. [ABSTRACT FROM AUTHOR]