Helen Webster

Operational prediction of ash concentrations in the distal volcanic cloud from the 2010 Eyjafjallajökull eruption

[1] During the 2010 eruption of Eyjafjallajokull, improvements were made to the modeling procedure at the Met Office, UK, enabling peak ash concentrations within the volcanic cloud to be estimated. In this paper we describe the ash concentration forecasting method, its rationale and how it evolved over time in response to new information and user requirements. The change from solely forecasting regions of ash to also estimating peak ash concentrations required consideration of volcanic ash emission rates, the fraction of ash surviving near-source fall-out, and the relationship between predicted mean and local peak ash concentrations unresolved by the model. To validate the modeling procedure, predicted peak ash concentrations are compared against observations obtained by ground-based and research aircraft instrumentation. This comparison between modeled and observed peak concentrations highlights the many sources of error and the uncertainties involved. Despite the challenges of predicting ash concentrations, the ash forecasting method employed here is found to give useful guidance on likely ash concentrations. Predicted peak ash concentrations lie within about one and a half orders of magnitude of the observed peak concentrations. A significant improvement in the agreement between modeled and observed values is seen if a buffer zone, accounting for positional errors in the predicted ash cloud, is used. Sensitivity of the predicted ash concentrations to the source properties (e.g., the plume height and the vertical distribution of ash at the source) is assessed and in some cases, seemingly minor uncertainties in the source specification have a large effect on predicted ash concentrations.

Simulated volcanic ash imagery: A method to compare NAME ash concentration forecasts with SEVIRI imagery for the Eyjafjallajökull eruption in 2010

[1] During volcanic eruptions that eject ash into the atmosphere Volcanic Ash Advisory Centers issue statements on the forecast dispersion of the ash so that the aviation industry can manage airspace to avoid aircraft encountering volcanic ash. Observations, such as those from satellites, are compared with the forecasts from an atmospheric dispersion model to assess the quality of the ash forecasts. A method has been developed to enable like-with-like comparison between satellite imagery of volcanic ash and simulated imagery using the forecast ash concentration data from an atmospheric dispersion model. The ash concentration and numerical weather prediction data are used as inputs to a radiative transfer model to simulate radiances. Simulated satellite images are created from these simulated radiances. Here, Spinning Enhanced Visible and Infrared Imager volcanic ash images based on infrared brightness temperatures for the Eyjafjallajokull eruption in 2010 are simulated. In addition to providing a useful tool for forecasters in a Volcanic Ash Advisory Center, the simulated images can be used to aid the understanding of how the ash affects the satellite imagery and also the physical properties of the ash.

Validation of a Lagrangian model plume rise scheme using the Kincaid data set

Correct prediction of the initial rise of a plume due to momentum and buoyancy effects is an important factor in dispersion modelling. A new plume rise scheme, based upon conservation equations of mass, momentum and heat, for the Lagrangian model, NAME, is described. The conservation equations are consistent with the well-known analytical plume rise formulae for both momentum- and buoyancy-dominated plumes. The performance of the new scheme is assessed against data from the Kincaid field experiment. Results show that the new scheme adds value to the model and significantly outperforms the previous plume rise scheme. Using data from assessments of atmospheric dispersion models using the Kincaid data set, it is shown that NAME is comparable to other models over short ranges.

Dry deposition modelling in a Lagrangian dispersion model

Revisions to the dry deposition scheme in the Lagrangian dispersion model, NAME, are described. Improvements are made for situations where material is not well mixed within the boundary layer and for sedimenting heavy particles. The revised scheme overcomes problems of computational noise, common in Lagrangian dispersion models, with a manageable or no increase to the computational cost. The result is a scheme that is well suited to all situations and that has better interaction between the advection, deposition and sedimentation.

Volcanic ash layer depth: Processes and mechanisms

The long duration of the 2010 Eyjafjallajokull eruption provided a unique opportunity to measure a widely dispersed volcanic ash cloud. Layers of volcanic ash were observed by the European Aerosol Research Lidar Network with a mean depth of 1.2 km and standard deviation of 0.9 km. In this paper we evaluate the ability of the Met Offices Numerical Atmospheric-dispersion Modelling Environment (NAME) to simulate the observed ash layers and examine the processes controlling their depth. NAME simulates distal ash layer depths exceptionally well with a mean depth of 1.2 km and standard deviation of 0.7 km. The dominant process determining the depth of ash layers over Europe is the balance between the vertical wind shear (which acts to reduce the depth of the ash layers) and vertical turbulent mixing (which acts to deepen the layers). Interestingly, differential sedimentation of ash particles and the volcano vertical emission profile play relatively minor roles.

Using data insertion with the NAME model to simulate the 8 May 2010 Eyjafjallajökull volcanic ash cloud

A data insertion method, where a dispersion model is initialized from ash properties derived from a series of satellite observations, is used to model the 8 May 2010 Eyjafjallajokull volcanic ash cloud which extended from Iceland to northern Spain. We also briefly discuss the application of this method to the April 2010 phase of the Eyjafjallajokull eruption and the May 2011 Grimsvotn eruption. An advantage of this method is that very little knowledge about the eruption itself is required because some of the usual eruption source parameters are not used. The method may therefore be useful for remote volcanoes where good satellite observations of the erupted material are available, but little is known about the properties of the actual eruption. It does, however, have a number of limitations related to the quality and availability of the observations. We demonstrate that, using certain configurations, the data insertion method is able to capture the structure of a thin filament of ash extending over northern Spain that is not fully captured by other modeling methods. It also verifies well against the satellite observations according to the quantitative object-based quality metric, SAL—structure, amplitude, location, and the spatial coverage metric, Figure of Merit in Space.

Using plume rise schemes to model highly buoyant plumes from large fires

The atmospheric dispersion model Numerical Atmospheric-dispersion Modelling Environment (NAME) is used to simulate the smoke plume from the explosion at the Buncefield oil depot. Simple modelling, in which the plume rise is included through an effective elevated source term, captures the transport and spread of the plume well. More complex modelling, using the NAME plume rise scheme, underestimates the plume rise and plume vertical spread. We consider a number of potential reasons for this underprediction and compare NAME predictions against large-eddy simulations (LES) of the plume.

Parametrizing unresolved mesoscale motions in atmospheric dispersion models

AbstractIn atmospheric dispersion models driven by meteorological data from numerical weather prediction (NWP) models, it is necessary to include a parameterization for plume spread that is due to unresolved mesoscale motions. These are motions that are not resolved by the input NWP data but are larger in size than the three-dimensional turbulent motions represented by turbulence parameterizations. Neglecting the effect of these quasi-two-dimensional unresolved mesoscale motions has been shown to lead to underprediction of plume spread and overprediction of concentrations within the plume. NWP modeling is conducted at a range of resolutions that resolve different scales of motion. This suggests that any parameterization of unresolved mesoscale motions should depend on the resolution of the input NWP data. Spectral analysis of NWP data and wind observations is used to assess the mesoscale motions unresolved by the NWP model. Appropriate velocity variances and Lagrangian time scales for these motions are fo...