The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profiles of the thermodynamic and kinematic state of the atmosphere in conjunction with other weather observations could significantly improve weather forecasting skill and resolution. High-resolution vertical measurements of pressure, temperature, humidity, wind speed and wind direction are critical to the understanding of atmospheric boundary layer processes integral to air–surface (land, ocean and sea ice) exchanges of energy, momentum, and moisture; how these are affected by climate variability; and how they impact weather forecasts and air quality simulations. We explore the potential value of collecting coordinated atmospheric profiles at fixed surface observing sites at designated times using instrumented UAS. We refer to such a network of autonomous weather UAS designed for atmospheric profiling and capable of operating in most weather conditions as a 3D Mesonet. We outline some of the fundamental and high-impact science questions and sampling needs driving the development of the 3D Mesonet and offer an overview of the general concept of operations. Preliminary measurements from profiling UAS are presented and we discuss how measurements from an operational network could be realized to better characterize the atmospheric boundary layer, improve weather forecasts, and help to identify threats of severe weather.

In July 2018, the University of Oklahoma deployed three CopterSonde remotely piloted aircraft systems (RPASs) to take measurements of the evolving thermodynamic and kinematic state of the atmospheric boundary layer (ABL) over complex terrain in the San Luis Valley, Colorado. A total of 180 flights were completed over 5 d, with teams operating simultaneously at two different sites in the northern half of the valley. A total of 2 d of operations focused on convection initiation studies, 1 d focused on ABL diurnal transition studies, 1 d focused on internal comparison flights, and the last day of operations focused on cold air drainage flows. The data from these coordinated flights provide insight into the horizontal heterogeneity of the atmospheric state over complex terrain. This dataset, along with others collected by other universities and institutions as a part of the LAPSE-RATE campaign, have been submitted to Zenodo (Greene et al., 2020) for free and open access (https://doi.org/10.5281/zenodo.3737087).

Freezing rain and freezing drizzle can produce nearly undetectable hazards, with potentially catastrophic consequences for aircraft within low altitudes (e.g., the terminal area). However, the lack of direct observations of the low-altitude freezing precipitation environment creates a challenge for forecasters, flight crews, dispatchers, and air traffic controllers. This research demonstrates how unmanned aerial vehicles (UAVs) can be designed and instrumented to create unmanned aerial weather measurement systems (WxUAS) capable of characterizing the low-altitude freezing precipitation environment and providing insight into the mechanisms that govern it. In this article, we discuss the design considerations for WxUAS-based in situ sampling during active precipitation. We present results from controlled experiments at the Oklahoma Mesonet’s calibration laboratory as well as results from intercomparison studies with collocated well-established ground-based instruments in Oklahoma and Colorado. Additionally, we explore the insights provided by high-resolution thermodynamic and cloud droplet size distribution profiles and their potential contributions to a better understanding of the low-altitude freezing precipitation environment.

This article assesses the individual and joint impact of pressure, temperature, and relative humidity on the accuracy of atmospheric CO₂ measurements collected by unmanned aerial systems (UASs) using low-cost commercial non-dispersive infrared (NDIR) sensors. We build upon previous experimental results in the literature and present a new dataset with increased gradients for each environmental variable to match the abrupt changes found in UAS-based atmospheric vertical profiles. As a key contribution, we present a low-complexity correction procedure to mitigate the impact of these variables and reduce errors in this type of atmospheric CO₂ measurement. Our findings support the use of low-cost NDIR sensors for UAS-based atmospheric CO₂ measurements as a complementary in situ tool for many scientific applications.

Winter precipitation forecasts of phase and amount are challenging, especially in Northeast United States where mixed precipitation events from various synoptic systems frequently occur. Yet, there are not enough quality observations of winter precipitation, particularly microphysical properties from falling snow or mixed phase precipitation. During the winters of 2021–2022, 2022–2023, and 2023–2024, the NASA Global Precipitation Measurement (GPM) Ground Validation (GV) program conducted a field campaign at the University of Connecticut (UConn). The goal of this campaign was to observe various phases of winter precipitation and winter storm types to validate the GPM satellite precipitation products. Over the three winters at UConn, a total of 40 instruments were deployed across two observing sites that captured 117 precipitation events, including 19 phase transition events as indicated by the PARSIVEL². These instruments included scanning and vertically pointing radars, along with suites of in-situ sensors. In addition, an unmanned aircraft system has been deployed in 2023–2024. Here, an overview of the different field deployments, instrumentation, and the datasets collected are presented. To showcase the observations, this article features a wide-ranging set of measurements collected from the instrument suite for the 28 February 2023 storm, during which six to eight inches of snow accumulated at the two different observing sites. Also included is a discussion on how these observations can be combined with other datasets to validate ground-based and remote sensing measurements and highlight important atmospheric processes that impact winter precipitation phase and amount. The datasets collected from this GPM GV field campaign are available at https://doi.org/10.5067/GPMGVUCONN/DATA101 (Cerrai et al., 2025).

In July 2018, the University of Oklahoma deployed three CopterSonde 2 remotely piloted aircraft systems (RPAS) to take measurements of the evolving thermodynamic and kinematic state of the atmospheric boundary layer (ABL) over complex terrain in the San Luis Valley, Colorado. A total of 180 flights were completed over five days, with teams operating simultaneously at two different sites in the northern half of the valley. Two days of operations focused on convection initiation studies, one day focused on ABL diurnal transition studies, one day focused on internal comparison flights, and the last day of operations focused on cold air drainage flows. The data from these coordinated flights provides insight into the horizontal heterogeneity of the atmospheric state over complex terrain as well as the expected horizontal footprint of RPAS profiles. This dataset, along with others collected by other universities and institutions as a part of the LAPSE-RATE campaign, have been submitted to Zenodo (Greene et al., 2020) for free and open access (https://doi.org/10.5281/zenodo.3737087).