We use a large‐eddy simulation model with a nested domain configuration (297 and 120 km wide) and an interactive land surface parameterization to simulate the complex population of shallow clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems campaign conducted in north‐central Oklahoma. Shallow convective clouds first formed over southeast Oklahoma and then spread toward the northwest into southern Kansas. By the early afternoon, the relatively uniform shallow cloud field became more complex in which some regions became nearly cloud free and in other regions larger shallow clouds developed with some transitioning to deeper, precipitating convection. We show that the model reproduces the observed heterogeneity in the cloud populations only when realistic variations in soil moisture are used to initialize the model. While more variable soil moisture and to a lesser extent cool lake temperatures drive the initial spatial heterogeneity in the cloud populations, precipitation‐driven cold pools become an important factor after 1300 CST. When smoother soil moisture variations are used in the model, more uniform shallow cloud populations are predicted with far fewer clouds that transition to deeper, precipitating convection that produce cold pools. An algorithm that tracks thousands of individual cumulus show that the more realistic soil moisture distributions produces clouds that are larger and have a longer lifetime. The results suggest that shallow and deep convection parameterizations used by mesoscale models need to account for the effects of variable land‐atmosphere interactions and cold pools.

Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.

The traffic-forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering and leaving peak periods. Accurate and real-time models are needed to approximate the nonlinear time-variant functions between system inputs and outputs from a continuous stream of training data. A proposed local linear regression model was applied to short-term traffic prediction. The performance of the model was compared with previous results of nonparametric approaches that are based on local constant regression, such as the k-nearest neighbor and kernel methods, by using 32-day traffic-speed data collected on US-290, in Houston, Texas, at 5-min intervals. It was found that the local linear methods consistently showed better performance than the k-nearest neighbor and kernel smoothing methods.

In this study, a higher-order turbulence closure scheme, called Cloud Layers Unified By Binormals (CLUBB), is implemented into a Multiscale Modeling Framework (MMF) model to improve low-cloud simulations. The performance of CLUBB in MMF simulations with two different microphysics configurations (one-moment cloud microphysics without aerosol treatment and two-moment cloud microphysics coupled with aerosol treatment) is evaluated against observations and further compared with results from the Community Atmosphere Model, Version 5 (CAM5) with conventional cloud parameterizations. CLUBB is found to improve low-cloud simulations in the MMF, and the improvement is particularly evident in the stratocumulus-to-cumulus transition regions. Compared to the single-moment cloud microphysics, CLUBB with two-moment microphysics produces clouds that are closer to the coast and agrees better with observations. In the stratocumulus-to-cumulus transition regions, CLUBB with two-moment cloud microphysics produces short-wave cloud forcing in better agreement with observations, while CLUBB with singlemoment cloud microphysics overestimates short-wave cloud forcing. CLUBB is further found to produce quantitatively similar improvements in the MMF and CAM5, with slightly better performance in the MMF simulations (e.g., MMF with CLUBB generally produces low clouds that are closer to the coast than CAM5 with CLUBB). Improved low-cloud simulations in MMF make it an even more attractive tool for studying aerosol-cloud-precipitation interactions.

The transition in a marine boundary layer (MBL) from stratocumulus topped to shallow cumulus topped is investigated by using a large eddy simulation (LES) model. The experiments performed aim to examine the influence on the transition of (1) the probability of buoyancy reversal at the MBL top (i.e. situations in which the mixture of two air parcels becomes denser than either of the original parcels due to phase change or other nonlinear processes involved in the mixing), and (2) the degree of decoupling in the MBL (i.e. the strength of a shallow stably stratified layer near cloud base). Our results suggest that a stratocumulus-topped MBL is most likely to transit to a cumulus-topped one when (1) there exists high probability of buoyancy reversal at the MBL top, and (2) the MBL is decoupled due to large surface evaporation. We argue that a parameterization that includes representation of those two effects combined has the potential to provide a simple way of predicting the MBL transition in climate models.

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud‐base mass flux, given a large‐scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud‐base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud‐base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge‐discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud‐base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

Results are presented of the GASS/EUCLIPSE single‐column model intercomparison study on the subtropical marine low‐level cloud transition. A central goal is to establish the performance of state‐of‐the‐art boundary‐layer schemes for weather and climate models for this cloud regime, using large‐eddy simulations of the same scenes as a reference. A novelty is that the comparison covers four different cases instead of one, in order to broaden the covered parameter space. Three cases are situated in the North‐Eastern Pacific, while one reflects conditions in the North‐Eastern Atlantic. A set of variables is considered that reflects key aspects of the transition process, making use of simple metrics to establish the model performance. Using this method, some longstanding problems in low‐level cloud representation are identified. Considerable spread exists among models concerning the cloud amount, its vertical structure, and the associated impact on radiative transfer. The sign and amplitude of these biases differ somewhat per case, depending on how far the transition has progressed. After cloud breakup the ensemble median exhibits the well‐known “too few too bright” problem. The boundary‐layer deepening rate and its state of decoupling are both underestimated, while the representation of the thin capping cloud layer appears complicated by a lack of vertical resolution. Encouragingly, some models are successful in representing the full set of variables, in particular, the vertical structure and diurnal cycle of the cloud layer in transition. An intriguing result is that the median of the model ensemble performs best, inspiring a new approach in subgrid parameterization.