Search

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. Plain Language Summary: Cold pools are cool gusty winds beneath thunderstorms that are formed by cooling from rainfall. They have many important impacts in the atmosphere and on society but are difficult to properly simulate in numerical weather models. The variability in cold pool temperature is an understudied feature of cold pools but which is important to represent in numerical models. In this study, we examine cold pool temperature variability from a dense network of surface weather station observations collected during a field campaign, and we compare those observations to numerical simulations of cold pools in a range of environments. We find that cold pools enhance temperature variability for distances greater than ∼5 km but suppress variability on smaller distances, and that the magnitude of cold pool temperature variability is strongly dependent on the environment and cold pool lifetime. We also show that numerical models, even at very high resolutions, are not able to properly simulate the magnitude of cold pool temperature variability. We highlight areas for improvement in numerical models that may help to improve simulations of cold pool variability, including land‐atmosphere interactions, turbulence, and conversion processes between water vapor and condensed water in storms. Key Points: Cold pool impacts on sub‐mesoscale temperature variability are quantified using variograms derived from observations and simulationsCold pools enhance temperature variability on scales between 5 and 15 km, but the magnitude varies strongly with lifetime and environmentHigh‐resolution case‐study and idealized simulations underestimate the magnitude of cold pool variability, irrespective of resolution [ABSTRACT FROM AUTHOR]

Abstract 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.

The diurnal cycle of environmental conditions for shallow and deep convection regimes within the Indian monsoon environment is investigated using comprehensive observations from the surface, boundary layer, and cloud layers. For shallow convection (SC) and deep convection (DC) in the afternoon hours, thermodynamics, moisture convergence, and several other environmental variables provide information on the factors that control the vertical extent of convection in both regimes. The SC regime is characterized by high sensible heat flux, leading to stronger boundary layer turbulence, a higher mixed layer, and increased dry air entrainment from the free troposphere. Evaluation of several pre-conditioning parameters with T-test statistics suggests that stronger mid-level meridional wind shear and lack of mid-level moistening are detrimental to the growth of clouds in the SC regime. Conversely, the DC regime is driven by low surface fluxes and low surface-level buoyancy, with higher boundary layer and mid-layer moisture and more mid-layer instability, supporting larger Convective Available Potential Energy (CAPE). Morning precursor conditions for the preference of SC versus DC regime in the afternoon reveal that total column water vapor, relative humidity and CAPE are significantly higher on DC days. Large-scale moisture transport in the early morning within and above the boundary layer, driven by stronger westerly winds, is a key factor for the development of DC later in the day. This investigation enhances our understanding of boundary layer controls on shallow and deep convection within the Indian Monsoon environment.