Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

Recent studies have shown that mercury (Hg) concentrations in tree rings have the potential to archive historical Hg exposure from local, regional, and global sources. The southeastern United States has received elevated Hg deposition, yet no studies have evaluated tree ring Hg in this region. Here, we quantify Hg accumulation and isotopic composition in tree rings collected in Shenandoah National Park, Virginia. Cores were collected from three individuals of three tree species—white oak (Quercus alba), northern red oak (Quercus rubra), and pitch pine (Pinus rigida)—within the northern, central, and southern areas of the Park (n = 27 cores). The cores were analyzed for Hg content in 10‐year increments, with some cores dating back to the early 1800s. Overall, tree ring Hg concentrations (ranging from below detection to 4.4 ng/g) were similar to other studies and varied between species, with pitch pine having higher concentrations than the deciduous species. The most notable feature of the tree ring Hg time series was a peak that occurred during the 1930s through 1950s, coinciding with the use of Hg at a local industrial facility. Atmospheric modeling indicates that potential emissions from the plant likely had a stronger impact on the southern region of the Park, consistent with the latitudinal gradient in tree ring Hg concentrations. Mass‐dependent and mass‐independent fractionation of Hg isotopes suggests contributions from both regional anthropogenic and local industrial sources during this period. This study demonstrates the potential usefulness of tree ring dendrochemistry for identifying historical sources of atmospheric Hg exposure.

Synoptic‐scale air quality climatologies can often be categorized into trajectory methods based on air parcel movement or air mass approaches that are primarily dictated by thermal and moisture conditions. Here, we examine the relationship between these two essentially independent methods of synoptic‐scale climate classification: 72‐h Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back‐trajectories and the Spatial Synoptic Classification (SSC) of air masses for Martinsburg, West Virginia, located at the northern end of the Shenandoah Valley, from 1997 to 2006. Statistically significant differences exist between trajectory groups across the six SSC air mass types within each season and between seasons within each air mass type.Further examination of the trajectory distributions reveals a high degree of overlap between trajectories associated with different air mass types. In some cases, up to 50% of one air mass's trajectories are statistically closer to the trajectories of a different air mass type. Accordingly, air mass alone cannot be used to identify specific source regions of pollutants. This suggests that, while an air mass classification system can be used to identify general flow patterns, the integration of both thermodynamic and air‐flow variables in a synoptic classification should provide information that is not discernible from either method independently. The inclusion of both back‐trajectory and air mass information represents the suite of environmental conditions that may enhance or suppress pollutant concentrations in the Shenandoah Valley better than using either measure independently. Copyright © 2009 Royal Meteorological Society

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 (R2 > 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 (R2 ≈ 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.