Abstract
Ash resuspension forecasts are only as good as the meteorological data that drives them. Nearsurface wind velocity can be affected by topography and is therefore sensitive to topographic resolution. The UK Met Office produces ash resuspension forecasts for southern Iceland using the Numerical Atmospheric-Dispersion Modelling Environment (NAME) model, assuming an ash source area based on the Eyjafjallajökull 2010 and Grímsvötn 2011 eruption deposits. Currently, a 10 km horizontal resolution meteorological and topographic data configuration is used. This study investigates the effect of horizontal resolution on resuspension modelling by comparing NAME forecasts made using the 10 km configuration and a 4 km configuration for a case study period from 18/09/2018 to 21/09/2018. Since NAME’s resuspension scheme depends strongly on wind friction velocity (𝑢*), 𝑢* data from both configurations were also compared.Model runs using the 4 km configuration predict around 2.3 times as much mass resuspended, with a wider plume. This is in part because a portion of the model source areas substantially released particles only when the 4 km configuration was used. The more complex topography of the 4 km resolution affected the surface wind vectors calculated by the model, for example channelling winds through valleys unresolved by the 10 km data. The distribution of wind friction velocity highs and lows also differed between resolutions, indicating that increasing resolution does not simply cause an increase in particle release rates or active source area size, but that the increased complexity allows for more localised effects to be modelled. Therefore, forecasts using higher resolution data are more accurate and could predict resuspension in areas that lower resolution forecasts would miss. The presence of many high wind friction velocity areas and observed resuspension events outside of the standard NAME resuspension source indicates that an updated source map should be considered.
| Date of Award | 12 May 2020 |
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| Original language | English |
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| Supervisor | Alison C Rust (Supervisor), Frances M Beckett (Supervisor) & Nicholas A Teanby (Supervisor) |