Convective clouds play an important role in Earth's climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. To improve predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled "Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE)." ESCAPE took place between 30 May and 30 September 2022 in the vicinity of Houston, Texas, because this area frequently experiences isolated deep convection that interacts with the region's mesoscale circulations and its range of aerosol conditions. ESCAPE focused on collecting observations of isolated deep convection through innovative sampling and developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars and one mobile Doppler lidar truck equipped with soundings were deployed from 30 May to 28 June. From 1 August to 30 September 2022, a dual-polarization C-band radar was deployed and operated using a novel, multisensor agile adaptive sampling strategy to track the entire life cycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle. SIGNIFICANCE STATEMENT: The ESCAPE field experiment provided unique observations of coastal convective cloud vertical motions, microphysics, and precipitation across a wide range of summertime aerosol and meteorological regimes. The highest aerosol concentrations occurred near the refineries in eastern Houston but did not contribute to the cloud condensation nuclei and ice-nucleating particles. The airborne measurements included frequent sampling of intense convective updraft dynamics and microphysics. A novel radar-based sampling of convective cells provided unique observations of their 3D structure throughout their life cycle. Mobile trucks equipped with soundings provided a detailed sampling of the sea-breeze structure and evolution. These datasets will be used for improving high-resolution simulations of high-impact events in coastal urbanized areas.

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 degrees 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 similar to 300 m above cloud base. However, new measurements with higher concentrations of aerosol and total cloud-base 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

Climate and numerical weather prediction models struggle to accurately predict radiative forcing over the Southern Ocean (SO), as the amount of clouds and their phases are poorly represented in such models due to a lack of observations upon which to base parameterizations. To address this, a novel particle identification (PID) scheme, based upon airborne radar and lidar data, was applied to data collected during the Southern Ocean Clouds, Aerosol, Radiation Transport Experimental Study (SOCRATES) to assess the vertical structure of SO boundary layer clouds. A comparison between the PID scheme and in situ phase data from SOCRATES showed relatively good agreement between the two data types. The convectivity of the clouds sampled during SOCRATES was determined using the novel Echo Classification from COnvectivity for Vertically pointing radars product. The PID and in situ data were then used synergistically to identify the following features of cloud vertical structure: (a) Supercooled liquid water was very common (Probability, P ∼ 80%) at cloud top for convective and stratiform clouds; (b) Supercooled large drops with maximum dimensions >95 μm frequently appear within a hundred meters below cloud top, particularly within convective clouds (Max P 35%–45%), but also within stratiform clouds (Max P 20%–30%); (c) Ice production was associated with convective activity, with P ∼ 20% at cloud top, increasing to 50%–70% 200 m below top, compared to P < 30% everywhere in stratiform clouds; (d) Convective clouds were found to be more vertically heterogeneous than stratiform clouds.

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.

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).CORRESPONDING AUTHOR: Gijs de Boer, gijs.deboer@colorado.edu

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.

Clouds pose many 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) and Cloud-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.

The accurate diagnosis of in-flight icing conditions is dependent on surface observations of cloud coverage, cloud base height, and surface precipitation type. However, the network for collecting these data over the the United States is neither contiguous nor evenly distributed. Surface observational gaps exist over much of the domain where in-flight icing conditions are diagnosed. Due to the way these observations are treated when diagnosing inflight icing conditions, the result is often circles where icing conditions are possible next to areas with no icing conditions diagnosed due to absence of surface observations. To avoid these visually unappealing and scientifically inconsistent artifacts, a method was developed to create surrogate surface observation data from numerical weather prediction model output. Using the individual condensate fields, accumulated precipitation, and temperature all three of the required datasets used from surface observations in diagnosing in-flight icing conditions were derived. The Current Icing Product (CIP) shows in-flight icing diagnoses created with the model derived surface observations that are similar to those created when using only real observations.

In-flight icing is a significant hazard in Alaska as the atmospheric environment is complex and ranges from maritime to continental and temperate to polar. An analysis of radiosonde data conditions for different climate zones reveals a high frequency of icing conditions year-round, varying with season and altitude. Many locations in Alaska depend on air travel for transportation, especially in smaller aircraft that fly at icing-prone altitudes. Thus, accurate diagnoses and forecasts of the icing environment, tuned to these varying conditions, are needed. Icing products are currently under development that are anticipated to meet the needs of aviation users in Alaska. The forecast product will be available first and is based on the Forecast Icing Product, originally developed for use in the CONUS, and predicts icing probability, supercooled large drop potential, and severity. The current spatial resolution is 13 km; high-resolution (3-km) model runs have also been used in the Alaska forecast algorithm to assess their value. An icing diagnosis algorithm that combines observations with model output, much like the Current Icing Product, is also in early development. To improve that product, the use of polar orbiting satellite data is being explored. These observations may be added to the diagnosis algorithm to provide observations where geostationary satellite data are not available.