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Mammatus clouds are an intriguing enigma of atmospheric fluid dynamics and cloud physics. Most commonly observed on the underside of cumulonimbus anvils, mammatus also occur on the underside of cirrus, cirrocumulus, altocumulus, altostratus, and stratocumulus, as well as in contrails from jet aircraft and pyrocumulus ash clouds from volcanic eruptions. Despite their aesthetic appearance, mammatus have been the subject of few quantitative research studies. Observations of mammatus have been obtained largely through serendipitous opportunities with a single observing system (e.g., aircraft penetrations, visual observations, lidar, radar) or tangential observations from field programs with other objectives. Theories describing mammatus remain untested, as adequate measurements for validation do not exist because of the small distance scales and short time scales of mammatus. Modeling studies of mammatus are virtually nonexistent. As a result, relatively little is known about the environment, formation mechanisms, properties, microphysics, and dynamics of mammatus. This paper presents a review of mammatus clouds that addresses these mysteries. Previous observations of mammatus and proposed formation mechanisms are discussed. These hypothesized mechanisms are anvil subsidence, subcloud evaporation/sublimation, melting, hydrometeor fallout, cloud-base detrainment instability, radiative effects, gravity waves, Kelvin--Helmholtz instability, Rayleigh--Taylor instability, and Rayleigh-Benard-like convection. Other issues addressed in this paper include whether mammatus are composed of ice or liquid water hydrometeors, why mammatus are smooth, what controls the temporal and spatial scales and organization of individual mammatus lobes, and what are the properties of volcanic ash clouds that produce mammatus? The similarities and differences between mammatus, virga, stalactites, and reticular clouds are also discussed. Finally, because much still remains to be learned, research opportunities are described for using mammatus as a window into the microphysical, turbulent, and dynamical processes occurring on the underside of clouds.

The International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week in July 2018 to demonstrate Unmanned Aircraft Systems’ (UAS) capabilities in sampling the atmospheric boundary layer. This week-long experiment was called the Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. Numerous remotely piloted aircrafts and ground-based instruments were deployed with the objective of capturing meso- and microscale phenomena in the atmospheric boundary layer. The University of Oklahoma deployed one Halo Streamline lidar and the University of Colorado Boulder deployed two Windcube lidars. In this paper, we use data collected from these Doppler lidars to estimate turbulence dissipation rate throughout the campaign. We observe large temporal variability of turbulence dissipation close to the surface with the Windcube lidars that is not detected by the Halo Streamline. However, the Halo lidar enables estimating dissipation rate within the whole boundary layer, where a diurnal variability emerges. We also find a higher correspondence in turbulence dissipation between the Windcube lidars, which are not co-located, compared to the Halo and Windcube lidar that are co-located, suggesting a significant influence of measurement volume on the retrieved values of dissipation rate. This dataset have been submitted to Zenodo (Sanchez Gomez and Lundquist, 2020) for free and open access (https://doi.org/10.5281/zenodo.4399967). [ABSTRACT FROM AUTHOR]

In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to showcase the role remotely-piloted aircraft systems (RPAS) can have in filling the atmospheric data gap. This campaign was dubbed Lower Atmospheric Process Studies at Elevation - A Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). In support of this campaign, ground-based remote and in-situ systems were also deployed for the campaign. The University of Oklahoma deployed the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), the University of Colorado deployed two Doppler wind lidars, and the National Severe Storms Lab deployed a Mobile Mesonet with the ability to launch radiosondes. This paper focuses on the data products from these instruments that result in profiles of the atmospheric state. The data are publicly available in the Zenodo LAPSE-RATE community portal (https://zenodo.org/communities/lapse-rate/). The profile data discussed are available at https://doi.org/10.5281/zenodo.3780623 (Bell and Klein, 2020), https://doi.org/10.5281/zenodo.3780593 (Bell et al., 2020b), https://doi.org/10.5281/zenodo.3727224 (Bell et al., 2020a), https://doi.org/10.5281/zenodo.3738175 (Waugh, 2020b), https://doi.org/10.5281/zenodo.3720444 (Waugh, 2020a), and https://doi.org/10.5281/zenodo.3698228 (Lundquist et al., 2020). [ABSTRACT FROM AUTHOR]

It is widely accepted that the atmospheric boundary layer is drastically under-sampled in the vertical dimension. In recent years, the commercial availability of ground based remote sensors combined with the widespread use of small, weather-sensing uncrewed aerial systems (W×UAS) has opened up many opportunities to fill this measurement gap. In July 2018, the University of Oklahoma (OU) deployed a state-of-the-art W×UAS dubbed the CopterSonde, as well as the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS), in the San Luis Valley in south-central Colorado. Additionally, these systems were deployed to the Kessler Atmospheric and Ecological Field Station (KAEFS) in October 2018. The co-location of these various systems provided ample opportunity to compare and contrast kinematic and thermodynamic observations from different methodologies of boundary layer profiling, namely: W×UAS, remote sensing, and the traditional in-situ radiosonde. In this study, data from these platforms are compared statistically from the two campaigns. Moreover, we present select instances from the data set to highlight differences between the measurement techniques. This analysis highlights strengths and weaknesses of PBL profiling and helps lay the groundwork for developing highly adaptable systems that integrate remote and in-situ profiling techniques. [ABSTRACT FROM AUTHOR]