Coincident profiling observations from Doppler lidars and radars are used to estimate the turbulence energy dissipation rate (ε) using three different data sources: (i) Doppler radar velocity (DRV), (ii) Doppler lidar velocity (DLV), and (iii) Doppler radar spectrum width (DRW) measurements. The agreement between the derived ε estimates is examined at the cloud base height of stratiform warm clouds. Collocated ε estimates based on power spectra analysis of DRV and DLV measurements show good agreement (correlation coefficient of 0.86 and 0.78 for both cases analyzed here) during both drizzling and nondrizzling conditions. This suggests that unified (below and above cloud base) time-height estimates of ε in cloud-topped boundary layer conditions can be produced. This also suggests that eddy dissipation rate can be estimated throughout the cloud layer without the constraint that clouds need to be nonprecipitating. Eddy dissipation rate estimates based on DRW measurements compare well with the estimates based on Doppler velocity but their performance deteriorates as precipitation size particles are introduced in the radar volume and broaden the DRW values. Based on this finding, a methodology to estimate the Doppler spectra broadening due to the spread of the drop size distribution is presented. The uncertainties in ε introduced by signal-to-noise conditions, the estimation of the horizontal wind, the selection of the averaging time window, and the presence of precipitation are discussed in detail.

Scattering models of precipitation‐size ice particles have shown that aggregates and spheroidal particles occupy distinct regions of the Ku‐Ka‐W‐band dual‐frequency ratio (DFR) plane. Furthermore, past ground‐based observations suggest that particle bulk density and characteristic size can be retrieved from the DFR plane. This study, for the first time, evaluates airborne DFR observations with coincident airborne microphysical measurements. Over 2 hr of microphysical data collected aboard the University of North Dakota Citation from the Olympic Mountains Experiment are matched with Airborne Precipitation and cloud Radar Third Generation triple‐frequency radar observations. Across all flights, 31% (63%) of collocated data points show nonspheroidal (spheroidal) particle scattering characteristics. DFR observations compared with in situ observations of effective density and particle characteristic size reveal relationships that could potentially be used to develop quantitative dual‐ and triple‐frequency DFR ice property retrievals.

A case of shallow cumulus and precipitating cumulus congestus sampled at the Atmospheric Radiation Measurement Program Southern Great Plains supersite during the Midlatitude Continental Convective Clouds Experiment is analyzed using a multisensor observational approach and numerical simulation. Observations from a new radar suite surrounding the facility are used to characterize the evolving statistical behavior of the precipitating cloud system. This is accomplished using distributions of different measures of cloud geometry and precipitation properties. Large-eddy simulation (LES) with size-resolved (bin) microphysics is employed to determine the forcings most important in producing the salient aspects of the cloud system captured in the radar observations. Our emphasis is on assessing the importance of time-varying versus steady state large-scale forcing on the model's ability to reproduce the evolutionary behavior of the cloud system. Additional consideration is given to how the characteristic spatial scale and homogeneity of the forcing imposed on the simulation influences the evolution of cloud system properties. Results indicate that several new scanning radar estimates such as distributions of cloud top are useful to differentiate the value of time-varying (or at least temporally well-matched) forcing on LES solution fidelity.

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft.A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including: numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large-drop formation (weather radar “first echo”). These measurements also complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2D) along-wind range–height indicator observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning Atmospheric Radiation Measurement Program (ARM) cloud radar (SACR) at the U.S. Department of Energy (DOE)–ARM Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger-scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous small nonprecipitating cloud elements. A new cloud identification and tracking algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2D observations (30 s) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud-element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived nonprecipitating clouds having an apparent life cycle shorter than 15 min. The advantages and disadvantages of cloud tracking using an SACR are discussed.