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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. 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.

Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site. Plain Language Summary: Aerosols on which water vapor condenses to form cloud droplets are commonly referred to as cloud condensation nuclei (CCN). The mechanisms responsible for changes in CCN concentrations over the open oceans are not fully known. The variability in the CCN concentrations over the Eastern North Atlantic region is examined. Analysis of day‐night differences in aerosol properties with wind direction show the aerosol measurements to be affected by human activity during certain wind conditions. Seven years of data collected when conditions were not impacted by human activity show higher CCN concentrations during summer months as compared to the winter month, and substantial variability within each month. CCN can be locally produced through wave‐breaking, transported to the location, and subsequently removed from the atmosphere as they transition into cloud or rain drops. Causes of seasonal and sub‐monthly changes in these CCN concentrations are examined. The analysis show transport of CCN to the site along with removal due to cloud processing to be the primary mechanisms for causing seasonal changes. In addition, air associated with low CCN concentration conditions at the site encountered higher cloudiness and rain prior to its arrival at the site as compared to that with high CCN concentrations. Key Points: Data suggest aerosol measurements at the Atmospheric Radiation Measurement Eastern North Atlantic site are affected by local emissions when the wind direction is between 90° and 310°Lower ambient wintertime Cloud Condensation Nuclei (CCN) concentrations compared to summer are primarily driven by advection and precipitationCCN variability at sub‐monthly timescales is driven by clouds and precipitation upstream of the site [ABSTRACT FROM AUTHOR]

Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of O(10) $\mathcal{O}(10)$ km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the "Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics," or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions. Plain Language Summary: Modern global weather models are accurate in predicting atmospheric processes at scales of around 10 km or larger, but are less good at predicting smaller scale processes, like for example, the interaction between solar radiation, individual clouds, and the resulting clouds shadows that are cast onto the land surface. High spatio‐temporal resolution research models are able to capture these smaller scale processes, but require a coupling to a weather model to account for the day‐to‐day variability in our weather. In this paper, we present a framework to couple large to small scale models, and demonstrate both the benefits and challenges of using this coupled model setup. The coupled setup excels in capturing the aforementioned high frequency interactions between small clouds and surface solar radiation. However, the chaotic nature of broken to overcast clouds is proven difficult to represent. The coupled model setup is published as open‐source code, and is therefore freely available to the research community. Key Points: We developed an open‐source Python package named (LS)2D, designed to downscale the ERA5 reanalysis with turbulence and cloud‐resolving large‐eddy simulation (LESs)One month long experiments with (LS)2D and MicroHH over the Netherlands demonstrate both the skill and limitations of the coupled setupCapturing high‐frequency interactions between clouds and surface solar irradiance requires high resolution (O $\mathcal{O}$(10) m) LES [ABSTRACT FROM AUTHOR]

Abstract Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of O(10) km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the “Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics,” or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions.

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts. Plain Language Summary: Predicting wildfire growth and smoke behavior is an important and historically difficult task. Here we compare a coupled fire‐weather model to current practices used in air quality forecasting in their ability to predict daily wildfire spread and fire intensity for the 2019 Williams Flats Fire. We also evaluate the fire‐weather model against high‐resolution satellite and aircraft measurements in ways previously unexplored. We find that including fire‐fighting efforts in our model makes the biggest improvements toward accurate wildfire forecasting. When we account for fire‐fighting efforts, fuel moisture maps, and increased fuel loads, our coupled fire‐weather model significantly outperforms simpler methods with reasonable computational time. Despite this, modeled fire intensity tends to lead observations by at least an hour. Optimal configuration options and avenues for forecasting improvements are discussed. Key Points: Adding containment lines into our wildfire model made the biggest difference in improving accuracy of daily burned area predictionsAccounting for containment, fuel moisture content maps, and increased fuel density consistently increased prediction skill above persistenceModeled fire radiative power leads observations by at least an hour, but with similar accuracy to prescribed diurnal cycles [ABSTRACT FROM AUTHOR]

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.

We used the observations from the east coast of the USA and model simulations to elicit marine atmospheric surface layer (MASL) processes that controlled the spatial variability of electromagnetic ducting conditions in the near‐shore (60 km) marine environment during CASPER‐East field campaign. The estimated evaporation duct height (EDH) showed an average decrease of ∼2.5 m over every 10 km distance from the shore. The surface layer stability conditions chiefly determined the duct heights. While the mean duct height remained ∼10 m under unstable conditions, deeper (>20 m) evaporation ducts were frequently evident (∼40% of the time) during stable MASL conditions, which were responsible for the observed near‐shore EDH variability. Episodes of warm offshore flow and advection of hot and humid air from the Gulfstream region created ideal environmental conditions for stable MASL development and thus deeper ducts over the coastal ocean. A detailed case study of warm and humid Gulfstream air advection over the cooler coastal ocean is presented to elucidate the temporal evolution of the surface layer stability and associated changes in the spatial distribution of the evaporation duct height. Key Points: Stable conditions in the nearshore CASPER‐East region can be caused by both offshore flow and flow from the Gulf StreamThe near‐shore region of Duck, North Carolina, showed a gradual decrease of evaporation duct height from the shore ∼2.5 m for every 10 kmHigh‐evaporation ducts are associated with stable stratifications in the coastal marine atmospheric surface layer [ABSTRACT FROM AUTHOR]