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

Wilkins, Kathryn; Watson, I. Matthew; Kristiansen, Nina Iren; Webster, Helen; Thomson, David J.; Dacre, Helen; Prata, A. J.

doi:10.1002/2015jd023895

Cited by 42 publications

(63 citation statements)

References 60 publications

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“…A better source term for the entire episode studied may be found by assimilating several satellite observations over the entire period studied. Wilkins et al (2016a) used an insertion method for ash forecasting by initializing a dispersion model with ash layers derived from retrievals of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG-2) satellite. The study found that the model field calculated by including up to six satellite observations gave a broader and more extensive ash cloud, which compared worse to the satellite observation on 8 May at 09:00 UTC than a single satellite retrieval inserted 6 h before the observation time.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

et al. 2017

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Abstract. Significant improvements in the way we can observe and model volcanic ash clouds have been obtained since the 2010 Eyjafjallajökull eruption. One major development has been the application of data assimilation techniques, which combine models and satellite observations such that an optimal understanding of ash clouds can be gained. Still, questions remain regarding the degree to which the forecasting capabilities are improved by inclusion of such techniques and how these improvements depend on the data input. This study explores how different satellite data and different uncertainty assumptions of the satellite and a priori emissions affect the calculated volcanic ash emission estimate, which is computed by an inversion method that couples the satellite retrievals and a priori emissions with dispersion model data. Two major ash episodes over 4 days in April and May of the 2010 Eyjafjallajökull eruption are studied. Specifically, inversion calculations are done for four different satellite data sets with different size distribution assumptions in the retrieval. A reference satellite data set is chosen, and the range between the minimum and maximum 4-day average load of hourly retrieved ash is 121 % in April and 148 % in May, compared to the reference. The corresponding a posteriori maximum and minimum emission sum found for these four satellite retrievals is 26 and 47 % of the a posteriori reference estimate for the same two periods, respectively. Varying the assumptions made in the satellite retrieval is seen to affect the a posteriori emissions and modelled ash column loads, and modelled column loads therefore have uncertainties connected to them depending on the uncertainty in the satellite retrieval. By further exploring our uncertainty estimates connected to a priori emissions and the mass load uncertainties in the satellite data, the uncertainty in the a priori estimate is found in this case to have an order-of-magnitudegreater impact on the a posteriori solution than the mass load uncertainties in the satellite. Part of this is explained by a too-high a priori estimate used in this study that is reduced by around half in the a posteriori reference estimate. Setting large uncertainties connected to both a priori and satellite mass load shows that they compensate each other, but the a priori uncertainty is found to be most sensitive. Because of this, an inversion-based emission estimate in a forecasting setting needs well-tested and well-considered assumptions on uncertainties for the a priori emission and satellite data. The quality of using the inversion in a forecasting environment is tested by adding gradually, with time, more observations to improve the estimated height versus time evolution of Eyjafjallajökull ash emissions. We show that the initially too-high a priori emissions are reduced effectively when using just 12 h of satellite observations. More satellite observations (> 12 h), in the Eyjafjallajökull case, place the volcanic injection at higher altitudes. Adding additional satellite...

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Section: Introductionmentioning

confidence: 99%

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

et al. 2017

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“…This metric individually considers aspects of the structure (S), amplitude (A), and location (L) of a forecast, revealing meaningful information about the systematic differences between forecasts. This diagnostic metric has been previously used to measure the skill of volcanic ash forecast using data insertion from 25 satellite observations ( Wilkins et al, 2016) and has been adapted here to compare the quality of on-line and off-line coupled NMMB-MONARCH-ASH forecasts. …”

Section: Quantitative Evaluation Scoresmentioning

confidence: 99%

“…Wilkins et al, 2016) to complement the SAL score for the evaluation of the spatial coverage between forecasts:…”

Section: Categorical Evaluation Scoresmentioning

confidence: 99%

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Marti¹,

Folch²

2017

Preprint

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Abstract.Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, 10 aviation, and climate. The efficiency of response and mitigation actions is directly associated to the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems where meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated to the off-line modeling systems has 15 received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the off-line coupling strategy are compared against those from an on-line forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects 20 of European (2010 Eyjafjallajökull) and South-American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors credited by off-line modeling are of the same order of magnitude as those associated to the source term uncertainties. In particular, traditional off-line forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70% of the ash cloud of an on-line forecast. These 25 inconsistencies are anticipated to be even more relevant in scenarios where the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient on-line dispersal models. 30Atmos. Chem. Phys. Discuss.,

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“…Another active field for STE applications is the estimation of the volcanic ash emissions. Many attempts have been made for several major volcano eruptions (Wen and Rose, 1994;Prata and Grant, 2001;Wilkins et al, 2014Wilkins et al, , 2016Chai et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

Chai¹,

Stein²,

Ngan³

2018

Preprint

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Abstract.A HYSPLIT inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2 for which the HYSPLIT has the best performance. Before introducing model uncertainty terms, the tests using concentration differences in the cost function results in severe underestimation while those using logarithm concentrations differences results in overestimation of the release rate. Adding model uncertainty 5 terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yield better results than calculating concentration differences and the estimates are more robust for a reasonable range of model uncertainty parameters. This is 10 further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters and the largest relative error among the six releases is 53.3%. At last, the system is tested for its capability to find a 15 single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all the six releases. However, the estimated location may have large errors. 1Geosci. Model Dev. Discuss., https://doi

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

Cited by 42 publications

References 60 publications

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Weak-constraint inverse modeling using HYSPLIT Lagrangian dispersion model and Cross Appalachian Tracer Experiment (CAPTEX) observations – Effect of including model uncertainties on source term estimation

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