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Cold pools formed by precipitating convective clouds are an important source of mesoscale temperature variability. However, their sub‐mesoscale (100 m–10 km) structure has not been quantified, impeding validation of numerical models and understanding of their atmospheric and societal impacts. We assess temperature variability in observed and simulated cold pools using variograms calculated from dense network observations collected during a field experiment and in high‐resolution case‐study and idealized simulations. The temperature variance in cold pools is enhanced for spatial scales between ∼5 and 15 km compared to pre‐cold pool conditions, but the magnitude varies strongly with cold pool evolution and environment. Simulations capture the overall cold pool variogram shape well but underestimate the magnitude of the variability, irrespective of model resolution. Temperature variograms outside of cold pool periods are represented by the range of simulations evaluated here, suggesting that models misrepresent cold pool formation and/or dissipation processes.

 

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.