Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation—a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.

The boundary layer plays a critical role in regulating energy and moisture exchange between the surface and the free atmosphere. However, the boundary layer and lower atmosphere (including shallow flow features and horizontal gradients that influence local weather) are not sampled at time and space scales needed to improve mesoscale analyses that are used to drive short-term model predictions of impactful weather. These data gaps are exasperated in remote and less developed parts of the world where relatively cheap observational capabilities could help immensely. The continued development of small, weather-sensing uncrewed aircraft systems (UAS), coupled with the emergence of an entirely new commercial sector focused on UAS applications, has created novel opportunities for partially filling this observational gap. This article provides an overview of the current level of readiness of small UAS for routinely sensing the lower atmosphere in support of national meteorological and hydrological services (NMHS) around the world. The potential benefits of UAS observations in operational weather forecasting and numerical weather prediction are discussed, as are key considerations that will need to be addressed before their widespread adoption. Finally, potential pathways for implementation of weather-sensing UAS into operations, which hinge on their successful demonstration within collaborative, multi-agency-sponsored testbeds, are suggested.

As a first step toward achieving full physics urban weather simulation capabilities within the resident‐GPU large‐eddy simulation (LES) FastEddy® model, we have implemented and verified/validated a method for explicit representation of building effects. Herein, we extend the immersed body force method (IBFM) from Chan and Leach (2007, https://doi.org/10.1175/2006JAMC1321.1) to (i) be scale independent and (ii) control building surface temperatures. Through a specific drag‐like term in the momentum equations, the IBFM is able to enforce essentially zero velocities within the buildings, in turn resulting in a no‐slip boundary condition at the building walls. In addition, we propose similar forcing terms in the energy and mass conservation equations that allow an accurate prescription of the building temperature. The extended IBFM is computationally efficient and has the potential to be coupled to building energy models. The IBFM exhibits excellent agreement with laboratory experiments of an array of staggered cubes at a grid spacing of mm, demonstrating the applicability of the method beyond the atmospheric scale. In addition, the IBFM is validated at atmospheric scale through simulations of downtown Oklahoma City ( m) using data collected during the Joint Urban 2003 (JU03) field campaign. Our LES IBFM results for mean wind speed, turbulence kinetic energy, and SF₆ transport and dispersion compare well to observations and produce turbulence spectra that are in good agreement with sonic anemometer data. Statistical performance metrics for the JU03 simulations are within the range of other LES models in the literature.

An enhanced National Center for Atmospheric Research (NCAR) integrated sounding system (ISS) was deployed as part of the Vertical Transport and Mixing (VTMX) field experiment, which took place in October of 2000. The enhanced ISS was set up at the southern terminus of the Great Salt Lake Valley just north of a gap in the Traverse Range (TR), which separates the Great Salt Lake and Utah Lake basins. This location was chosen to sample the dynamic and thermodynamic properties of the flow as it passes over the TR separating the two basins. The enhanced ISS allowed for near-continuous sampling of the nocturnal boundary layer (NBL) and low-level winds associated with drainage flow through the gap in the TR. Diurnally varying winds were observed at the NCAR site on days characterized by weak synoptic forcing and limited cloud cover. A down-valley jet (DVJ) was observed on about 50% of the nights during VTMX, with the maximum winds usually occurring within 150 m of the surface. The DVJ was associated with abrupt warming at low levels as a result of downward mixing and vertical transport of warm air from the inversion layer above. Several processes were observed to contribute to vertical transport and mixing at the NCAR site. Pulses in the strength of the DVJ contributed to vertical transport by creating localized areas of low-level convergence. Gravity waves and Kelvin–Helmholtz waves, which facilitated vertical mixing near the surface and atop the DVJ, were observed with a sodar and an aerosol backscatter lidar that were deployed as part of the enhanced ISS. The nonlocal nature of the processes responsible for generating turbulence in strongly stratified surface layers in complex terrain confounds surface flux parameterizations typically used in mesoscale models that rely on Monin–Obukhov similarity theory. This finding has major implications for modeling NBL structure and drainage flows in regions of complex terrain.

Over the last few years, the concept of incorporating aerial vehicles into the urban environment for diverse purposes has attracted ample interest and investment. These purposes cover a broad spectrum of applications, from larger vehicles designed for passenger transport, to package delivery and inspection/surveillance missions performed by small unmanned drones. While these Advanced Air Mobility (AAM) operations have the potential to alleviate bottlenecks arising from saturated surface transportation networks, there are a number of challenges that need to be addressed to make these operations safe and viable. One challenge is predicting weather effects within the urban environment with the required level of spatiotemporal fidelity, which current operational weather models fail to provide due to the use of coarse grid spacings (a few kilometers) constrained by the predictive performance limitations of traditional computer architectures. Herein, we demonstrate how FastEddy®, a microscale model that exploits the accelerated nature of graphics processing units for high‐performance computing, can be used to understand and predict urban weather impacts from seasonal, day‐to‐day, diurnal, and sub‐hourly scales. To that end, we efficiently perform more than 50 telescoped simulations of microscale urban effects at street‐scale (5 m grid spacing) driven by realistic weather over a 20 km² region centered at the downtown area of Dallas, Texas. Our analyses demonstrate that urban‐weather interactions at the street‐scale are complex and tightly connected, which is of utmost relevance to AAM operations. These demonstrations reveal the capability of such models to provide real‐time weather hazard avoidance products tailored to capture microscale urban effects.

The simulation dataset described herein provides a high-resolution depiction of the four-dimensional variability of weather conditions across the northern half of the San Luis Valley, Colorado, during the 14–20 July 2018 Lower Atmospheric Profiling Studies at Elevation-A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field program. The simulations explicitly resolved phenomena (e.g., wind shift boundaries, vertical shear, strong thermals, turbulence in the boundary layer, fog, low ceilings and thunderstorms) that are potentially hazardous to small uncrewed aircraft system (UAS) operations. Details of the model configuration used to perform the simulations and the data-processing steps used to produce the final grids of state variables and other sensible weather products (e.g., ceiling and visibility, turbulence) are given. A nested (WRF) model configuration was used in which the innermost domain featured large-eddy-permitting 111 m grid spacing. The simulations, which were executed twice per day, were completed in under 6 h on the National Center for Atmospheric Research (NCAR) Cheyenne supercomputer using 59 cores (2124 processors). A few examples are provided to illustrate model skill at predicting fine-scale boundary layer structures and turbulence associated with drainage winds, up-valley flows and convective storm outflows. A subset of the data is available at the Zenodo data archive (https://zenodo.org/record/3706365#.X8VwZrd7mpo, Pinto et al., 2020b) while the full dataset is archived on the NCAR Digital Asset Services Hub (DASH) and may be obtained at https://doi.org/10.5065/83r2-0579 (Pinto et al., 2020a).