Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption
Quantifying the impact of meteorological uncertainty on emission estimates and the risk to aviation using source inversion for the Raikoke 2019 eruption
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DOI：
10.5194/acp-22-8529-2022
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发表时间：
2022-07
影响因子：
6.3
通讯作者：
N. Harvey;H. Dacre;C. Saint;A. Prata;H. Webster;R. Grainger
中科院分区：
文献类型：
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作者：
N. Harvey;H. Dacre;C. Saint;A. Prata;H. Webster;R. Grainger
抽象的。由于许多火山位置偏远，火山爆发时释放的火山灰的时间、数量和垂直分布情况存在很大的不确定性。确定这些特性的一种方法是将联合收割机事先估计与卫星检索和大气扩散模型的模拟相结合，以使用称为源反演的技术创建后验排放估计，该后验排放估计受观测和事先估计的约束。然而，结果不仅取决于事先假设的准确性、大气扩散模型和所使用的观测，而且还取决于扩散模拟中使用的气象数据的准确性。在这项研究中，我们通过使用来自气象局全球和区域包围预测系统的气象数据集合来表示气象数据中的不确定性，并将其应用于2019年的Raikoke喷发，从而推进了源反演方法。从Himawari-8卫星的检索与NAME扩散模型模拟相结合，以创建后验排放估计。集合气象学的使用提供了信心，在事后排放估计和相关的分散模拟，用于产生灰预测。根据羽流高度观测，先前对Raikoke喷发的细火山灰排放量的平均估计比任何平均后集合估计高出15倍以上。此外，后验估计有不同的垂直分布，27%-44%的火山灰被排放到平流层，而平均先验估计为8%。这对火山灰的远距离迁移产生了影响，因为从大气层的这一区域沉积到地表需要很长的时间。后验集合传播表示灰排放的反演估计中的不确定性。在喷发后的第一个48小时，之前的灰柱载荷位于与一组独立的卫星检索相关的误差估计之外，而后合奏柱载荷没有。将基于风险的方法应用于使用后验排放的分散模拟的集合表明，基于集合成员超过预定义的灰浓度阈值的分数，被认为对航空具有最高风险的区域减少了49%。这是比较估计使用合奏的分散模拟使用以前的排放与合奏气象。如果在Raikoke火山爆发后使用震源反演，就有可能大大减少对航空业务的干扰。后反演排放量估计也敏感的不确定性，在其他喷发源参数和内部扩散模型参数。扩展集合反演方法以考虑这些参数的不确定性，将更全面地了解排放的不确定性，进一步提高这些估计的可信度。
Abstract. Due to the remote location of many volcanoes, there is substantial uncertainty about the timing, amount and vertical distribution of volcanic ash released when they erupt. One approach to determine these properties is to combine prior estimates with satellite retrievals and simulations from atmospheric dispersion models to create posterior emission estimates, constrained by both the observations and the prior estimates, using a technique known as source inversion. However, the results are dependent not only on the accuracy of the prior assumptions, the atmospheric dispersion model and the observations used, but also on the accuracy of the meteorological data used in the dispersion simulations. In this study, we advance the source inversion approach by using an ensemble of meteorological data from the Met Office Global and Regional Ensemble Prediction System to represent the uncertainty in the meteorological data and apply it to the 2019 eruption of Raikoke. Retrievals from the Himawari-8 satellite are combined with NAME dispersion model simulations to create posterior emission estimates. The use of ensemble meteorology provides confidence in the posterior emission estimates and associated dispersion simulations that are used to produce ash forecasts. Prior mean estimates of fine volcanic ash emissions for the Raikoke eruption based on plume height observations are more than 15 times higher than any of the mean posterior ensemble estimates. In addition, the posterior estimates have a different vertical distribution, with 27 %–44 % of ash being emitted into the stratosphere compared to 8 % in the mean prior estimate. This has consequences for the long-range transport of ash, as deposition to the surface from this region of the atmosphere happens over long timescales. The posterior ensemble spread represents uncertainty in the inversion estimate of the ash emissions. For the first 48 h following the eruption, the prior ash column loadings lie outside an estimate of the error associated with a set of independent satellite retrievals, whereas the posterior ensemble column loadings do not. Applying a risk-based methodology to an ensemble of dispersion simulations using the posterior emissions shows that the area deemed to be of the highest risk to aviation, based on the fraction of ensemble members exceeding predefined ash concentration thresholds, is reduced by 49 %. This is compared to estimates using an ensemble of dispersion simulations using the prior emissions with ensemble meteorology. If source inversion had been used following the eruption of Raikoke, it would have had the potential to significantly reduce disruptions to aviation operations. The posterior inversion emission estimates are also sensitive to uncertainty in other eruption source parameters and internal dispersion model parameters. Extending the ensemble inversion methodology to account for uncertainty in these parameters would give a more complete picture of the emission uncertainty, further increasing confidence in these estimates.