[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 Eyjafjallajökull 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.

This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long-range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajökull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajökull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.

Abstract. There is a large discrepancy between the size of volcanic ash particles measured on the ground at least 500 km from their source volcano (known as cryptotephra) and those reported by satellite remote sensing (effective radius of 0.5–9 μm; 95% of particles < 17 μm diameter). Here we present new results from the fields of tephrochronology (a dating technique based on volcanic ash layers), dispersion modelling and satellite remote sensing in an attempt to understand why. A literature review and measurements of prehistoric and recent eruptions were used to characterise the size range of cryptotephra grains. Icelandic cryptotephra deposited in NW Europe has lognormal particle size distributions (PSDs) with median lengths of 20–70 μm (geometric standard deviation: 1.40–1.66; 95th percentile length: 42–126 μm). Grain-size range estimates from the literature are similar. We modelled the settling of volcanic ash using measured fall velocities of ash particles, a release height typical of moderate Icelandic eruptions (10 km), and a wind speed typical for NW Europe (10 m s−1), to show that an ash cloud can transport particles up to 80 μm diameter up to 850 km in 24 h. Thus, even moderately sized Icelandic eruptions can be expected to deposit cryptotephra on mainland Europe. Using simulated satellite infrared data for dispersion-model-derived ash clouds, we demonstrate a systematic bias towards small grain sizes in retrievals of volcanic ash clouds that contain large proportions of cryptotephra-sized grains. As the median radius of the simulated PSD increases, fewer ash-containing pixels are correctly identified. Where retrievals are made of simulated clouds with mass median radii larger than ~ 10 μm, the mean retrieved reff plateaus at around 9 μm. Assuming Mie scattering by dense spheres when interpreting satellite infrared brightness temperature difference (BTD) data puts an upper limit on retrieved particle sizes. If larger, irregularly shaped ash grains can also produce a BTD effect, this will result in further underestimation of grain size, e.g. in coarse ash clouds close to a volcano.

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.

Abstract. There is a large discrepancy between the size of volcanic ash particles measured from deposits on the ground (known as cryptotephra; 20–125 μm in length) and those reported by satellite remote sensing (effective radii of 0.5–9 μm; 95% of particles < 17 μm diameter). We use results from the fields of tephrochronology (a dating technique based on volcanic ash layers), dispersion modelling and satellite remote sensing in an attempt to understand from where it arises. We show that Icelandic cryptotephras deposited in NW Europe have lognormal particle size distributions (PSDs) with median lengths of 20–70 μm (geometric standard deviation: 1.40–1.66; 95th percentile length: 42–126 microns). This is consistent with semi-quantitative grainsize range estimates from the literature. Using measured fall velocities of ash particles, a release height typical of moderate Icelandic eruptions (10 km) and a wind speed typical for NW Europe (10 m s−1), we find that an ash cloud can transport particles < 80 μm diameter up to 850 km in 24 h, so that even moderately sized Icelandic eruptions can deposit cryptotephra on mainland Europe. The proportion of cryptotephra in airborne clouds is unknown. We used simulated satellite data of dispersion-model-derived ash clouds to investigate the effect of PSD on satellite retrievals and show that as the median radius of the input PSD increases, fewer ash-containing pixels are correctly identified. Where retrievals are made of simulated clouds with mass median radii larger than ~ 10 μm, the mean retrieved reff plateaus at around 9 μm. This is a systematic bias in the retrieval algorithm that would cause the grainsize of distal clouds containing significant cryptotephra to be underestimated. This cannot explain discrepancies in coarser proximal clouds, however, which may be because the complex physics of scattering by highly irregularly-shaped grains is inadequately represented by assuming that particles are dense spheres.