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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. 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 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. 

Abstract Observations of the air vertical velocities ( w air ) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C 3 LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated w air values are 36.2 and 49.9 m s −1 , respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based w air values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based w air values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest w air values, dual-Doppler methods generally produced 15–20 m s −1 lower w air for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns.