Spatial evaluation of volcanic ash forecasts using satellite observations

Harvey, Nicholas Peter; Dacre, Helen

doi:10.5194/acp-16-861-2016

Cited by 21 publications

(22 citation statements)

References 28 publications

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“…The Fractional Skill Score was originally developed to determine the skill of weather forecast models to represent radar rainfall observations (Roberts, 2008;Roberts and Lean, 2008;Mittermaier et al, 2013), but has been since used to also describe the skill of dispersion models in representing volcanic clouds (Dacre et al, 2016;Harvey and Dacre, 2016). For volcanic SO 2 clouds, the Fractional Skill Score is calculated using the ratio between the model-simulated (M k ) and observed (O k ) fractional coverage of the SO 2 cloud at each location (neighbourhood) in the domain investigated.…”

Section: Fractional Skill Score (Fss)mentioning

confidence: 99%

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The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Leeuw¹,

Schmidt²,

Witham³

et al. 2020

Preprint

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Abstract. Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 &pm; 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

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Section: Fractional Skill Score (Fss)mentioning

confidence: 99%

“…In the case of a total mismatch FSS equals to zero. In general for the FSS, a model-simulation is considered to have skill when FSS > 0.5 (see e.g., Harvey and Dacre, 2016).…”

Section: Fractional Skill Score (Fss)mentioning

confidence: 99%

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Leeuw¹,

Schmidt²,

Witham³

et al. 2020

Preprint

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show abstract

“…Roberts and Lean 2008;Mittermaier and Roberts 2010), it has also been used to evaluate modelled cloud brightness temperatures (e.g. Griffin et al 2017) and volcanic ash plumes (Harvey and Dacre 2016). The FSS is calculated for different regimes of interest (e.g.…”

Section: Fractions Skill Scorementioning

confidence: 99%

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

Gabey

Grimmond

Capel-Timms

2018

Theor Appl Climatol

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Anthropogenic heat flux (Q F ) may be significant in cities, especially under low solar irradiance and at night. It is of interest to many practitioners including meteorologists, city planners and climatologists. Q F estimates at fine temporal and spatial resolution can be derived from models that use varying amounts of empirical data. This study compares simple and detailed models in a European megacity (London) at 500 m spatial resolution. The simple model (LQF) uses spatially resolved population data and national energy statistics. The detailed model (GQF) additionally uses local energy, road network and workday population data. The Fractions Skill Score (FSS) and bias are used to rate the skill with which the simple model reproduces the spatial patterns and magnitudes of Q F , and its sub-components, from the detailed model. LQF skill was consistently good across 90% of the city, away from the centre and major roads. The remaining 10% contained elevated emissions and Bhot spots^representing 30-40% of the total city-wide energy. This structure was lost because it requires workday population, spatially resolved building energy consumption and/or road network data. Daily total building and traffic energy consumption estimates from national data were within ± 40% of local values. Progressively coarser spatial resolutions to 5 km improved skill for total Q F , but important features (hot spots, transport network) were lost at all resolutions when residential population controlled spatial variations. The results demonstrate that simple Q F models should be applied with conservative spatial resolution in cities that, like London, exhibit timevarying energy use patterns.

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“…Identifying when, where, and how much simulations diverge can be accomplished in many ways and is related to model forecast verification. There are a number of spatial forecast verification methods in use for dispersion model forecasts [32][33][34][35] and any of the measures could be tracked over time to produce similar information to that found in Figure 7. For example, ref.…”

Section: Identify Where and When Simulations Divergementioning

confidence: 99%

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

Crawford

2020

Atmosphere

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Atmospheric Lagrangian particle dispersion models, LPDM, simulate the dispersion of passive tracers in the atmosphere. At the most basic level, model output consists of the position of computational particles and the amount of mass they represent. In order to obtain concentration values, this information is then converted to a mass distribution via density estimation. To date, density estimation is performed with a nonparametric method so that output consists of gridded concentration data. Here we introduce the use of Gaussian mixture models, GMM, for density estimation. We compare to the histogram or bin counting method for a tracer experiment and simulation of a large volcanic ash cloud. We also demonstrate the use of the mixture model for automatic identification of features in a complex plume such as is produced by a large volcanic eruption. We conclude that use of a mixture model for density estimation and feature identification has potential to be very useful.

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Spatial evaluation of volcanic ash forecasts using satellite observations

Cited by 21 publications

References 28 publications

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Anthropogenic heat flux: advisable spatial resolutions when input data are scarce

The Use of Gaussian Mixture Models with Atmospheric Lagrangian Particle Dispersion Models for Density Estimation and Feature Identification

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