Examining the influence of meteorological simulations forced by different initial and boundary conditions in volcanic ash dispersion modelling

Mulena, Gabriela Celeste; Allende, David Gabriel; Puliafito, Salvador Enrique; Lakkis, Susan Gabriela; Cremades, Pablo; Ulke, Ana Graciela

doi:10.1016/j.atmosres.2016.02.009

Cited by 15 publications

(7 citation statements)

References 69 publications

(65 reference statements)

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“…offline coupling). Recent studies have revealed a high sensitivity of ash simulations to initialization data [71] and that online coupling (i.e. ash advection and deposition calculations at every meteorological model time step) is required to capture changes in the wind fields that affect ash dispersal and deposition [27,30], which can improve volcanic ash transport modeling [72].…”

Section: Forecast Methodologymentioning

confidence: 99%

Experimental High-Resolution Forecasting of Volcanic Ash Hazard at Sakurajima, Japan

Poulidis¹,

Takemi²,

Iguchi³

2019

JDR

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A high-resolution forecast methodology for the ash hazard at Sakurajima volcano, Japan, is presented. The methodology employs a combined modeling approach and utilizes eruption source parameters estimated by geophysical observations from Sakurajima, allowing for a proactive approach in forecasting. The Weather Research and Forecasting (WRF) model is used to downscale Japan Meteorological Agency (JMA) forecast data over the area of interest. The high-resolution meteorological data are then used in FALL3D model to provide a forecast for the ash dispersal and deposition. The methodology is applied for an eruption that occurred on June 16, 2018. Disdrometer observations of ashfall are used along with ash dispersal modeling to inform the choice of the total grain size distribution (TGSD). A series of pseudo-forecast ash dispersal simulations are then carried out using the proposed methodology and estimated TGSD, initialized with meteorological forecast data released up to ∼13 hours before the eruption, with results showing surprising consistency up to ∼10 hours before the eruption. Using forecast data up to 4 hours before the eruption was seen to constrain observation to model ratios within a factor of 2–4 depending on the timing of simulation and location. A number of key future improvements for the methodology are also highlighted.

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Section: Forecast Methodologymentioning

confidence: 99%

Experimental High-Resolution Forecasting of Volcanic Ash Hazard at Sakurajima, Japan

Poulidis¹,

Takemi²,

Iguchi³

2019

JDR

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“…CC BY 4.0 License. a strong research focus on improving the simulation of volcanic ash in ADMs and measuring volcanic ash using in-situ and satellite measurement techniques (e.g., Witham et al, 2007;Corradini et al, 2011;Prata and Prata, 2012;Mulena et al, 2016;Harvey et al, 2018;Webster et al, 2020). The skill of ADMs in simulating the evolution of SO 2 clouds has also been investigated (e.g., Eckhardt et al, 2008;Heard et al, 2012;Boichu et al, 2013;, but to a much lesser extent as this has generally been the realm of global climate models interested in the climatic impacts of the periodic stratospheric injections from volcanoes (e.g., Haywood et al, 2010;Solomon et al, 2011;Schmidt et al, 2018).…”

Section: Introductionmentioning

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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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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“…P. Kumar et al () indicated that the model performance on meteorological fields initialized from the ECMWF data set was superior to that initialized from the NCEP data sets over the Indian region. The effect of different meteorological analysis data sets on volcanic ash dispersion (Mulena et al, ) and biomass burning smoke transport (Ge et al, ) has also been investigated. However, overall effect of the two data sets on simulated ambient pollutant concentrations in addition to meteorological fields over India for a whole year has not yet been studied.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

Chatani

Sharma

2018

JGR Atmospheres

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Many places in India suffer from severe air pollution. Regional air quality simulations are essential to develop effective strategies for improving air quality, considering the nonlinear relationships between ambient pollutants and their precursor emissions. Meteorological fields used in simulations are derived from regional meteorological models with analysis data sets as inputs. This study reveals that two major analysis data sets provided by the National Centers for Environmental Prediction and the European Centre for Medium‐Range Weather Forecasts (ECMWF) cause significant differences in simulated meteorological fields over India. Especially, relative humidity values simulated using the ECMWF data set are much higher and closer to the observed values than those simulated using the National Centers for Environmental Prediction data set. Results simulated using the ECMWF data set show better model performance for most meteorological parameters over India. Differences in relative humidity originate in the data contained in the analysis data sets through grid nudging. It is not possible to avoid this underestimation by simply turning off grid nudging. The meteorological fields simulated with two major analysis data sets also lead to differences in pollutant concentrations simulated by regional chemical transport models through various physical and chemical processes. Differences originating in the two analysis data sets could be comparable with the uncertainties originating in various emission inventories in some regions and seasons in India. However, discrepancies between observed and simulated pollutant concentrations cannot be explained only by differences of the meteorological fields. Other aspects need to be explored for better performance required to develop effective strategies for India, based on accurate regional air quality simulations.

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Examining the influence of meteorological simulations forced by different initial and boundary conditions in volcanic ash dispersion modelling

Abstract: Examining the influence of meteorological simulations forced by different initial and boundary conditions in volcanic ash dispersion modelling,

Cited by 15 publications

References 69 publications

Experimental High-Resolution Forecasting of Volcanic Ash Hazard at Sakurajima, Japan

Experimental High-Resolution Forecasting of Volcanic Ash Hazard at Sakurajima, Japan

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

Uncertainties Caused by Major Meteorological Analysis Data Sets in Simulating Air Quality Over India

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