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Multirotor drones (part of the category of small Uncrewed Aerial Systems [sUAS] or small Uncrewed Aerial Vehicles [sUAV]) are used in atmospheric research to make measurements of the lower atmosphere, and their use is poised to increase in the future. New drone atmospheric sensing opportunities, such as ride-along applications and drone swarms, are emerging. These opportunities, which may not allow room for specialized shielding or aspiration equipment, together with increased drone usage, necessitate the characterization of the performance of unshielded sensors mounted to drones if the accuracy of such observations is to be understood. In this work, we characterize the accuracy of thermodynamic measurements, specifically temperature and water vapor mixing ratio, based on the sensor mounting position onboard multirotor drones. To assess the influence of the drone mechanics on the measurements, ninety-eight individual drone flights with eight distinct thermodynamic sensor positions were performed next to an instrumented flux tower and a tethersonde carrying identical sensors, where the tower and tethersonde measurements are assumed as truth. The flights were at least nine minutes in length, and nine of the flights were conducted at night. At the best position, absolute daytime temperature errors were between -0.83K and +0.61K at the 95% confidence interval, while nighttime temperature errors were smaller, ranging from -0.28 K and +0.48 K. Water vapor mixing ratio errors are within -0.22 g kg-1 and +0.66 g kg-1. We conclude that measurements in field campaigns are more accurate when sensors are placed away from the main body of the drone and are sufficiently aspirated, such as a position near, but not directly under, a spinning propeller. 

Abstract. Multirotor drones (part of the category of small Uncrewed Aerial Systems [sUAS] or small Uncrewed Aerial Vehicles [sUAV]) are used in atmospheric research to make measurements of the lower atmosphere, and their use is poised to increase in the future. New drone atmospheric sensing opportunities, such as ride-along applications and drone swarms, are emerging. These opportunities, which may not allow room for specialized shielding or aspiration equipment, together with increased drone usage, necessitate the characterization of the performance of unshielded sensors mounted to drones if the accuracy of such observations is to be understood. In this work, we characterize the accuracy of thermodynamic measurements, specifically temperature and water vapor mixing ratio, based on the sensor mounting position onboard multirotor drones. To assess the influence of the drone mechanics on the measurements, ninety-eight individual drone flights with eight distinct thermodynamic sensor positions were performed next to an instrumented flux tower and a tethersonde carrying identical sensors, where the tower and tethersonde measurements are assumed as truth. The flights were at least nine minutes in length, and nine of the flights were conducted at night. At the best position, absolute daytime temperature errors were between −0.83 and +0.61 K at the 95 % confidence interval, while nighttime temperature errors were smaller, ranging from −0.28 and +0.48 K. Water vapor mixing ratio errors are within −0.22 and +0.66 g kg−1. We conclude that measurements in field campaigns are more accurate when sensors are placed away from the main body of the drone and are sufficiently aspirated, such as a position near, but not directly under, a spinning propeller. 

Abstract Heterogeneous landscapes can influence the development of convection through the generation of thermally driven mesoscale circulations. To assess the impacts of these circulations and their interaction with sea breezes, we simulated convection in an idealized coastal environment using the Regional Atmospheric Modeling System (RAMS). We compared simulations with striped patterns of surface vegetation to those of uniform vegetation to identify the importance of vegetation heterogeneity in impacting convective development. Under dry soil conditions representative of those during the Tracking Aerosol Convection Interactions Experiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) campaigns in June 2022, we found that these vegetation-induced circulations, referred to in the literature as “forest breezes,” are more important than the sea breeze in determining the location of convection initiation. Convection and precipitation are also found to be favored over forests and suppressed over pasture and suburban landscapes as a result of greater surface sensible heat flux over the forest. Our findings also indicate that forest breezes are important for initiating convection along the boundaries of the forest, but that cold pools may play a key role in propagating the forest breezes toward the center of the forest stripe. In our simulations, the collisions of these breezes in the center of the forest stripe lead to uplift and strong convection there; however, a different width of the forest stripe would alter when the forest breezes collide or whether they collide at all. The presence of these cold pools may therefore impact the “ideal stripe width,” the width of each vegetation stripe which maximizes domain-wide precipitation. 

Abstract In the Northeast and Great Lakes regions of the United States, the influence of multiple lakes on the overlying air can greatly affect lake-effect snowfall over downwind communities. To assess the impact of Lake Huron on snowfall downwind of Lake Erie, we simulated a lake-effect snow event which occurred from 1 to 6 January 2010 using the Regional Atmospheric Modeling System (RAMS). We found that the presence of Lake Huron enhances snowfall downwind of Lake Erie by almost 20% and leads to much heavier local snowfall totals than when Lake Huron is not present. This increase in snowfall is due to a lake-to-lake (L2L) convective band, as secondary circulations associated with lake-effect convection form over Lake Huron and persist overland between the lakes before reintensifying over Lake Erie. As these secondary circulations move over Lake Erie, lifting within the upward branch of the circulations accelerates the development of convection within the L2L band. Surface fluxes and convection over Lake Huron deepen the boundary layer, promoting deeper vertical development of this L2L band over Lake Erie. However, although this boundary layer modification strengthens the L2L band, we found that it actually reduced lake-effect snowfall produced by wind-parallel bands (WPB) in other parts of the lake. This indicates that boundary layer modification from upstream lakes may impact L2L bands differently than WPB which are not connected to upstream secondary circulations. 

Abstract The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely, U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from three-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory’s convection permitting Warn-on-Forecast System (WoFS). A parametric regression technique using the sinh–arcsinh–normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65, and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50%. Meanwhile, the area of the 5 and 10 m s⁻¹updraft cores shows an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity, which could be useful in assessing a storm’s severe potential. Significance StatementAll convective storm hazards (tornadoes, hail, heavy rain, straight line winds) can be related to a storm’s updraft. Yet, there is no direct measurement of updraft speed or area available for forecasters to make their warning decisions from. This paper addresses the lack of observational data by providing a machine learning solution that skillfully estimates the maximum updraft speed within storms from only the radar reflectivity 3D structure. After further vetting the machine learning solutions on additional real-world examples, the estimated storm updrafts will hopefully provide forecasters with an added tool to help diagnose a storm’s hazard potential more accurately. 

Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. 

Abstract. Optimizing radar observation strategies is one of the mostimportant considerations in pre-field campaign periods. This is especiallytrue for isolated convective clouds that typically evolve faster than theobservations captured by operational radar networks. This study investigatesuncertainties in radar observations of the evolution of the microphysicaland dynamical properties of isolated deep convective clouds developing inclean and polluted environments. It aims to optimize the radar observationstrategy for deep convection through the use of high-spatiotemporalcloud-resolving model simulations, which resolve the evolution of individualconvective cells every 1 min, coupled with a radar simulator and a celltracking algorithm. The radar simulation settings are based on the TrackingAerosol Convection Interactions ExpeRiment (TRACER) and Experiment of SeaBreeze Convection, Aerosols, Precipitation and Environment (ESCAPE) fieldcampaigns held in the Houston, TX, area but are generalizable to other fieldcampaigns focusing on isolated deep convection. Our analysis produces thefollowing four outcomes. First, a 5–7 m s−1 median difference inmaximum updrafts of tracked cells is shown between the clean and pollutedsimulations in the early stages of the cloud lifetimes. This demonstratesthe importance of obtaining accurate estimates of vertical velocity fromobservations if aerosol impacts are to be properly resolved. Second,tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than oneradar in addition to the operational volume scans. We also propose the useof a single-RHI (range height indicator) updraft retrieval technique for cellsclose to the radars, for which multi-Doppler radar retrievals are stillchallenging. Finally, increasing the number of deep convective cells sampledby such observations better represents the median maximum updraft evolutionwith sample sizes of more than 10 deep cells, which decreases the errorassociated with sampling the true population to less than 3 m s−1. 

Abstract Vertical velocities and microphysical processes within deep convection are intricately linked, having wide-ranging impacts on water and mass vertical transport, severe weather, extreme precipitation, and the global circulation. The goal of this research is to investigate the functional form of the relationship between vertical velocity (w) and microphysical processes that convert water vapor into condensed water (M) in deep convection. We examine an ensemble of high-resolution simulations spanning a range of tropical and midlatitude environments, a variety of convective organizational modes, and different model platforms and microphysics schemes. The results demonstrate that the relationship betweenwandMis robustly linear, with the slope of the linear fit being primarily a function of temperature and secondarily a function of supersaturation. TheR²of the linear fit is generally above 0.6 except near the freezing and homogeneous freezing levels. The linear fit is examined both as a function of local in-cloud temperature and environmental temperature. The results for in-cloud temperature are more consistent across the simulation suite, although environmental temperatures are more useful when considering potential observational applications. The linear relationship betweenwandMis substituted into the condensate tendency equation and rearranged to form a diagnostic equation forw. The performance of the diagnostic equation is tested in several simulations, and it is found to diagnose the storm-scale updraft speeds to within 1 m s⁻¹throughout the upper half of the clouds. Potential applications of the linear relationship betweenwandMand the diagnosticwequation are discussed. 

Abstract The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas, on 19 June 2013, are examined using multiple cloud-resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet–nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell-tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 min). Most of the CRMs produced similar aerosol loading impacts on the warm phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the life cycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable among CRMs and included increases or decreases in ice amounts at inconsistent stages of the cell life cycle and midlevel versus upper-level changes in ice. This intermodel variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations. 

Abstract The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model parameterizations, grid resolution, and the lack of appropriate observations with which to evaluate such simulations. The overarching goal of the Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C 3 LOUD-Ex) was to enhance our understanding of deep convective storm processes and their representation within numerical models. To address this goal, a field campaign was conducted during July 2016 and May–June 2017 over northeastern Colorado, southeastern Wyoming, and southwestern Nebraska. Pivotal to the experiment was a novel “Flying Curtain” strategy designed around simultaneously employing a fleet of uncrewed aerial systems (UAS; or drones), high-frequency radiosonde launches, and surface observations to obtain detailed measurements of the spatial and temporal heterogeneities of cold pools. Updraft velocities were observed using targeted radiosondes and radars. Extensive datasets were successfully collected for 16 cold pool–focused and seven updraft-focused case studies. The updraft characteristics for all seven supercell updraft cases are compared and provide a useful database for model evaluation. An overview of the 16 cold pools’ characteristics is presented, and an in-depth analysis of one of the cold pool cases suggests that spatial variations in cold pool properties occur on spatial scales from O (100) m through to O (1) km. Processes responsible for the cold pool observations are explored and support recent high-resolution modeling results.