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The secondary ice process (SIP) is a major microphysical process, which can result in rapid enhancement of ice particle concentration in the presence of preexisting ice. SPICULE was conducted to further investigate the effect of collision–coalescence on the rate of the fragmentation of freezing drop (FFD) SIP mechanism in cumulus congestus clouds. Measurements were conducted over the Great Plains and central United States from two coordinated aircraft, the NSF Gulfstream V (GV) and SPEC Learjet 35A, both equipped with state-of-the-art microphysical instrumentation and vertically pointing W- and Ka-band radars, respectively. The GV primarily targeted measurements of subcloud aerosols with subsequent sampling in warm cloud. Simultaneously, the Learjet performed multiple penetrations of the ascending cumulus congestus (CuCg) cloud top. First primary ice was typically detected at temperatures colder than −10°C, consistent with measured ice nucleating particles. Subsequent production of ice via FFD SIP was strongly related to the concentration of supercooled large drops (SLDs), with diameters from about 0.2 to a few millimeters. The concentration of SLDs is directly linked to the rate of collision–coalescence, which depends primarily on the subcloud aerosol size distribution and cloud-base temperature. SPICULE supports previous observational results showing that FFD SIP efficiency could be deduced from the product of cloud-base temperature and maximum diameter of drops measured ~300 m above cloud base. However, new measurements with higher concentrations of aerosol and total cloudbase drop concentrations show an attenuating effect on the rate of coalescence. The SPICULE dataset provides rich material for validation of numerical schemes of collision–coalescence and SIP to improve weather prediction simulations

Uncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy.

An automated quality control preprocessing algorithm for removing nonweather radar echoes in airborne Doppler radar data has been developed. This algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data assimilation. The algorithm uses the editing functions in the Solo software package developed by the National Center for Atmospheric Research to remove noise, Earth-surface, sidelobe, second-trip, and other artifacts. The characteristics of these nonweather radar returns, the algorithm to identify and remove them, and the impacts of applying different threshold levels on wind retrievals are presented. Verification was performed by comparison with published Electra Doppler Radar (ELDORA) datasets that were interactively edited by different experienced radar meteorologists. Four cases consisting primarily of convective echoes from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX), Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX), Hurricane Rainband and Intensity Change Experiment (RAINEX), and The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS08) field experiments were used to test the algorithm using three threshold levels for data removal. The algorithm removes 80%, 90%, or 95% of the nonweather returns and retains 95%, 90%, or 85% of the weather returns on average at the low-, medium-, and high-threshold levels. Increasing the threshold level removes more nonweather echoes at the expense of also removing more weather echoes. The low threshold is recommended when weather retention is the highest priority, and the high threshold is recommended when nonweather removal is the highest priority. The medium threshold is a good compromise between these two priorities and is recommended for general use. Dual-Doppler wind retrievals using the automatically edited data compare well to retrievals from interactively edited data.

Because of a lack of regular, direct measurements, little information is available about the frequency and spatial and temporal distribution of icing conditions aloft, including supercooled large drops (SLD). Research aircraft provide in situ observations of these conditions, but the sample set is small and can be biased. Other techniques must be used to create a more unbiased climatology. The presence and absence of icing and SLD aloft can be inferred using surface weather observations in conjunction with vertical profiles of temperature and moisture. In this study, such a climatology was created using 14 yr of coincident, 12-hourly Canadian and continental U.S. surface weather reports and balloonborne soundings. The conditions were found to be most common along the Pacific Coast from Alaska to Oregon, and in a large swath from the Canadian Maritimes to the Midwest. Prime locations migrated seasonally. Most SLD events appeared to occur below 4 km, were less than 1 km deep, and were formed via the collision–coalescence process.

Clouds posemany operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) andCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.

Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.

To gain new perspectives on vertical and horizontal structures, and to sample the atmosphere in difficult-to-reach environments, unmanned aircraft systems (UAS) have been highlighted in several community reports (e.g., U.S. Department of Energy 2015; National Academies of Sciences, Engineering and Medicine 2018) as offering substantial promise. Thanks to support from the U.S. National Science Foundation, the U.S. Department of Energy, and the International Union of Geodesy and Geophysics (IUGG) International Association of Meteorology and Atmospheric Sciences (IAMAS), 42 of the participants were provided with some level of financial support to help offset meeting costs. [...]the session on the use of UAS in severe weather research included presentations highlighting the use of UAS to measure and understand supercell thunderstorms and tropical cyclones. The overview highlighted that these efforts were the direct result of collaborative efforts between a variety of geographically collocated entities, specifically including campus departments (e.g., Aerospace Engineering, Atmospheric and Oceanic Sciences, Geography); institutes and laboratories, including the university’s Cooperative Institute for Research in Environmental Sciences (CIRES), the National Center for Atmospheric Research (NCAR), and the NOAA Earth System Research Laboratory; and campus projects, such as the Integrated Remote and In Situ Sensing (IRISS) campus grand challenge project.

Using observations from research aircraft flights over the Great Lakes region, synoptic and mesoscale environments that appear to drive a relationship between liquid water content, drop concentration, and drop size are investigated. In particular, conditions that fell within “small drop” and “large drop” regimes are related to cloud and stability profiles, providing insight regarding whether the clouds are tied to the local boundary layer. These findings are supported by analysis of flight data from other parts of North America and used to provide context for several icing incidents and accidents where large-drop icing was noted as a contributing factor. The relationships described for drop size discrimination in continental environments provide clues that can be applied for both human- and model-generated icing forecasts, as well as automated icing algorithms.