In this survey we consider the impact of turbulence on cloud formation from the cloud scale to the droplet scale. We assess progress in understanding the effect of turbulence on the condensational and collisional growth of droplets and the effect of entrainment and mixing on the droplet spectrum. The increasing power of computers and better experimental and observational techniques allow for a much more detailed study of these processes than was hitherto possible. However, much of the research necessarily remains idealized and we argue that it is those studies which include such fundamental characteristics of clouds as droplet sedimentation and latent heating that are most relevant to clouds. Nevertheless, the large body of research over the last decade is beginning to allow tentative conclusions to be made. For example, it is unlikely that small‐scale turbulent eddies (i.e. not the energy‐containing eddies) alone are responsible for broadening the droplet size spectrum during the initial stage of droplet growth due to condensation. It is likely, though, that small‐scale turbulence plays a significant role in the growth of droplets through collisions and coalescence. Moreover, it has been possible through detailed numerical simulations to assess the relative importance of different processes to the turbulent collision kernel and how this varies in the parameter space that is important to clouds. The focus of research on the role of turbulence in condensational and collisional growth has tended to ignore the effect of entrainment and mixing and it is arguable that they play at least as important a role in the evolution of the droplet spectrum. We consider the role of turbulence in the mixing of dry and cloudy air, methods of quantifying this mixing and the effect that it has on the droplet spectrum. Copyright © 2012 Royal Meteorological Society and British Crown Copyright, the Met Office

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

We consider large-eddy simulation (LES) of buoyant plumes in uniform and stably stratified environments. We show that in the former case the results agree well with the simple plume model of Morton, Taylor & Turner (Proc. R. Soc. Lond. A, vol. 234, 1956, p. 1). In particular, we calculate an entrainment constant which is consistent with laboratory and field measurements and find no significant difference between the radial spreading rates of vertical velocity and buoyancy. In a stably stratified environment, the LES plume shows better agreement with Morton et al. (1956) below the level at which the buoyancy first vanishes than above this level. Above the level of neutral buoyancy, the LES plume is characterized by an ascending core of negative buoyancy surrounded by a descending annulus of positive buoyancy. We compare the LES data with the model of Bloomfield & Kerr (J. Fluid Mech., vol. 424, 2000, p. 197), which explicitly accounts for these coherent motions. The model exhibits many qualitative aspects of the LES plume and quantitative agreement can be improved by adjusting the downward volume flux relative to the upward volume flux in a manner consistent with the LES plume. This simple adjustment, along with revised values of the entrainment constants, represents the combined effects of an overturning region at the top of the plume (where a fluid element reverses direction), ‘plume-top’ entrainment (whereby the plume entrains ambient fluid above the plume) as well as lateral entrainment and detrainment processes (both external and internal) occurring above the top of the model plume.

We analyze the sensitivity of a mathematical model of volcanic ash dispersion in the atmosphere to the representation of key physical processes. These include the parameterization of subgrid‐scale atmospheric processes and source parameters such as the height of the eruption column, the mass emission rate, the size of the particulates, and the amount of ash that falls out close to the source. By comparing the results of the mathematical model with satellite and airborne observations of the ash cloud that erupted from Eyjafjallajökull volcano in May 2010, we are able to gain some insight into the processes and parameters that govern the long‐range dispersion of ash in the atmosphere. The structure of the ash cloud, particularly its width and depth, appears to be sensitive to the source profile (i.e., whether ash is released over a deep vertical column or not) and to the level of subgrid diffusion. Of central importance to the quantitative estimates of ash concentration in the distal ash cloud is the fallout of ash close to the source. By comparing the mass of the ash and the column loadings in the modeled and observed distal ash cloud, we estimate the fraction of fine ash that survives into the distal ash cloud albeit with considerable uncertainty. The processes that contribute to this uncertainty are discussed.

We have developed an aggregation scheme for use with the Lagrangian atmospheric transport and dispersion model NAME (Numerical Atmospheric Dispersion modelling Environment), which is used by the London Volcanic Ash Advisory Centre (VAAC) to provide advice and guidance on the location of volcanic ash clouds to the aviation industry. The aggregation scheme uses the fixed pivot technique to solve the Smoluchowski coagulation equations to simulate aggregation processes in an eruption column. This represents the first attempt at modelling explicitly the change in the grain size distribution (GSD) of the ash due to aggregation in a model which is used for operational response. To understand the sensitivity of the output aggregated GSD to the model parameters, we conducted a simple parametric study and scaling analysis. We find that the modelled aggregated GSD is sensitive to the density distribution and grain size distribution assigned to the non-aggregated particles at the source. Our ability to accurately forecast the long-range transport of volcanic ash clouds is, therefore, still limited by real-time information on the physical characteristics of the ash. We assess the impact of using the aggregated GSD on model simulations of the 2010 Eyjafjallajökull ash cloud and consider the implications for operational forecasting. Using the time-evolving aggregated GSD at the top of the eruption column to initialize dispersion model simulations had little impact on the modelled extent and mass loadings in the distal ash cloud. Our aggregation scheme does not account for the density of the aggregates; however, if we assume that the aggregates have the same density of single grains of equivalent size, the modelled area of the Eyjafjallajökull ash cloud with high concentrations of ash, significant for aviation, is reduced by ∼ 2 %, 24 h after the start of the release. If we assume that the aggregates have a lower density (500 kg m⁻³) than the single grains of which they are composed and make up 75 % of the mass in the ash cloud, the extent is 1.1 times larger.