Abstract. Small uncrewed aircraft systems (sUxS) are now being routinely used not only for sampling atmospheric boundary layer (ABL) processes and land-atmosphere interactions but also have significant potential to improve weather forecasting at National Weather Service (NWS) Weather Forecast Offices (WFOs). In the present study, we used observations obtained from a Meteomatics Meteodrone SSE sUxS flown on 31 days between 20 August and 10 December 2020 near Oliver Springs, Tennessee, located 35 km northwest of Knoxville, Tennessee. We flew the sUxS up to 700 m above ground level, starting around sunrise and continuing every half hour until 3.5–4.0 hours past sunrise under synoptically quiescent, fair weather conditions. These datasets were provided in real time to the local NWS WFO in Morristown, Tennessee and used by forecasters there to assist with short-term operational forecasting needs. The sUxS profiles also provided finescale details on the early-morning transition over complex terrain and how this evolution varied during the late summer to winter period, which can be used to support the initialization of numerical weather prediction models.

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 (II), 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.

Improvements in numerical weather and climate modeling depend on accurate accounting of land-atmosphere interactions and the biological processes that mediate them at multiple spatial and temporal scales.Unfortunately, there is a problematic, persistent mismatch between the scales of observations and models.The substantial heterogeneity of the land surface means that observations do not always accurately reflect the entire model grid cell.Therefore, spatial and temporal scaling of surface fluxes is fundamental to how we evaluate theories on what happens within the subgrid of atmospheric models and how it feeds back onto larger-scale dynamics.This scaling is thus fundamental to assessing the parameterizations that represent land-atmosphere interactions in atmospheric models.

The thermodynamic properties of the atmospheric boundary layer (ABL) play an important role in several atmospheric processes such as convection initiation, turbulence mixing, the exchange of heat and momentum, and cloud-microphysics. Since the ABL depth (henceforth, BLD) defines the volume of the ABL, many studies consider BLD to be a key scaling parameter to understand and quantify ABL mixing processes. However, most of these studies attributed both BLD temporal and horizontal variability on various scales solely to the impact of underlying surface forcing via locally generated buoyancy fluxes and static stability. We argue that the impact of horizontal advection is often neglected yet important for a more thorough understanding of ABL thermodynamics and kinematics. Here we identified four potentially advection-dominated ABL regimes across (1) the urban-rural interface, (2) complex terrain and adjacent plains, (3) the land-sea interface where horizontal transport of marine boundary layer airmasses influences the regional ABL over coastal areas, and (4) frontal environments where mid-latitude cyclones affect ABL processes via passages of cold and warm frontal boundaries. We then introduced a conceptual framework based on observations so that ABL processes are explained not only by surface forcing but also by horizontal advection of mass, momentum, and energy. This work will help advance our understanding of ABL processes and single out potential sources that trigger drastic changes in ABL thermodynamic features including the BLDs under diverse horizontal advection environments.

Focal Area: In this work we seek to improve surface-layer parameterizations of heat, moisture, and momentum exchange for use in numerical weather prediction (NWP) models . We will use data from a variety of sources and locations as input into a physics-guided neural network-driven spatiotemporal sequence forecasting method to develop a new approach for calculating these land-atmosphere interactions.

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

The scientific community is beginning to see how our environment reacts to changes on an unprecedented time and space scale with the utilization of small Unmanned Aircraft Systems or sUAS. These new observation platforms can be utilized for flood forecasting, local weather forecasting, monitor wildlife, improve hurricane forecasts and this the tip of the iceberg. This technology is a new tool that will allow the scientific community to observe the environment on time and space scales that are unprecedented. This particular talk will primarily address the future of these observing platforms as it relates to advancing the atmospheric sciences. UAS’s are rapidly becoming the new technology that can acquire low-level environment information more frequently, in support of higher-resolution model forecasts of severe thunderstorm and tornado potential, improvement in Environmental Model Prediction, provide environmental information to provide better support the spread of wildfires and smoke, as well as wildfire imagery for Incident Command and more complete/accurate storm damage surveys. One of the end goals would be to have a nationwide network of sUAS providing near-continuous observations of thermodynamic parameters, NDVI, surface sensible heat and wind speed and direction. Most of these observations are being done on a regular basis and some will be attainable in the future as technology progresses and National Airspace becomes more accessible.

Since drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were <1 mm, with most absolute daily differences similar to 5 mm. The most general characteristics were for a net dry bias in the PRISM data in the Southwest and a net moist bias in the southern United States. Verifying the PRISM dataset provides us with confidence it can be used with estimates of evapotranspiration, high-resolution gridded soil properties, and vegetation datasets to produce a daily gridded soil moisture product for operational use in the analyses and prediction of drought and excessive soil moisture conditions.

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 zeta and the dimensionless wind shear phi(m), temperature gradient phi(h), and moisture gradient phi(q) as well as relationships between bulk Richardson number Rib, friction coefficient Cu, heat-transfer coefficient C-t, and moisture-transfer coefficient C-r. 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 zeta 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.

Agriculture is the main source of ammonia (NH3) emissions in the atmosphere. NH3 is precursor to secondary fine particulate matter, which is of concern for its impacts on health and visibility. There are a limited number of field measurements of NH3 emissions from fertilizer application in the US, and this limits our understanding of the importance of individual NH3 source and sink processes in controlling timing and magnitude of NH3 emissions. In this study, a new parameterization of the effect of urease inhibitor on NH3 emissions from urea based fertilizer was developed on the basis of experimental results found in the literature. This parameterization was combined with an existing operational parameterization of soil and stomatal emission potentials (Gamma(g), Gamma(s)) and was implemented in a surface-atmosphere transfer model for NH3 (SURFATM-NH3) in order to evaluate the bi-directional fluxes of NH3 at the field scale. The model was evaluated with field measurements obtained by the flux-gradient (FG) and relaxed eddy accumulation (REA) methods in a fertilized corn field in central Illinois. By integrating the effect of urease inhibitor, the timing of the highest NH3 emission peak was successfully predicted and its magnitude was close to that measured (predicted 2106 ng m(-2) s(-1), measured by FG 2312 +/- 582 ng m(-2) s(-1)). Based on the model results, urease inhibitor has a considerable effect on the dynamics and order of magnitude of NH3 fluxes. Furthermore, the model simulated the inhibiting action of N-(n-butyl) thiophosphoric (nBTPT) and suggests that it can reduce NH3 volatilization by 32%. The model also successfully predicted environmental parameters, such as soil temperature. Finally, this new version of SURFATM-NH3 is a valuable tool to estimate the NH3 bi-directional fluxes at the field scale, which describes dynamic modeling of Gamma(s) and Gamma(g) by taking into account the effect of urease inhibitor which is commonly used in the US to improve the efficiency of urea fertilizers.

We present unique observations of a total solar eclipse from a small unmanned aircraft system (sUAS) platform that was operated during the 21 August 2017 North American solar eclipse. The observations were collected near Ten Mile, Tennessee, where eclipse totality lasted 2min 38s. A 2-m micrometeorological tripod was erected on-site to measure surface and air temperature, near-surface water vapour, incoming and outgoing shortwave and longwave radiative fluxes, and turbulent fluxes. The sUAS platform and micrometeorological tripod observations indicate significant cooling below a height of 50m above ground level (a.g.l.) during and shortly after totality. Near-surface temperatures do not return to pre-eclipse values until about 60min following totality. Above about 50m a.g.l., smaller temperature changes are observed during the eclipse, as the duration of the eclipse has less influence on deeper boundary-layer turbulence. Additionally, the sensible heat flux becomes slightly negative around totality, and the turbulence kinetic energy and vertical velocity variance concurrently decrease. The evolution of the near-surface meteorological fields are investigated in more detail using a large-eddy simulation (LES) model. The simulations generally reproduce the observations well, in terms of the timing and magnitude of changes in temperature, moisture and sensible and latent heat fluxes. However, the LES model slightly underestimates the diurnal range and decrease in temperature during the eclipse while overestimating the sensible heat fluxes.

Observations of near-surface vertical wind profiles and vertical momentum fluxes obtained from a Doppler lidar and instrumented towers deployed during VORTEX-SE in the spring of 2017 are analyzed. In particular, departures from the predictions of Monin–Obukhov similarity theory (MOST) are documented on thunderstorm days, both in the warm air masses ahead of storms and within the cool outflow of storms, where MOST assumptions (e.g., horizontal homogeneity and a steady state) are least credible. In these regions, it is found that the nondimensional vertical wind shear near the surface commonly exceeds predictions by MOST. The departures from MOST have implications for the specification of the lower boundary condition in numerical simulations of convective storms. Documenting departures from MOST is a necessary first-step toward improving the lower boundary condition and parameterization of near-surface turbulence (“wall models”) in storm simulations.

The High-Resolution Rapid Refresh (HRRR) model became operational at the National Centers for Environmental Prediction (NCEP) in 2014 but the HRRR's performance over certain regions of the coterminous United States has not been well studied. In the present study, we evaluated how well version 2 of the HRRR, which became operational at NCEP in August 2016, simulates the near-surface meteorological fields and the surface energy balance at two locations in northern Alabama. We evaluated the 1-, 3-, 6-, 12-, and 18-h HRRR forecasts, as well as the HRRR's initial conditions (i.e., the 0-h initial fields) using meteorological and flux observations obtained from two 10-m micrometeorological towers installed near Belle Mina and Cullman, Alabama. During the 8-month model evaluation period, from 1 September 2016 to 30 April 2017, we found that the HRRR accurately simulated the observations of near-surface air and dewpoint temperature (R-2 > 0.95). When comparing the HRRR output with the observed sensible, latent, and ground heat flux at both sites, we found that the agreement was weaker (R-2 approximate to 0.7), and the root-mean-square errors were much larger than those found for the near-surface meteorological variables. These findings help motivate the need for additional work to improve the representation of surface fluxes and their coupling to the atmosphere in future versions of the HRRR to be more physically realistic.

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.

The authors wish to make the following correction to this paper [...]

Measurements taken by an automated national meteorological monitoring network during the 2017 total solar eclipse illuminate how the land and atmosphere respond to a sudden loss of sunlight.