Estimation and propagation of volcanic source parameter uncertainty in an ash transport and dispersal model: application to the Eyjafjallajokull plume of 14-16 April 2010
Estimation and propagation of volcanic source parameter uncertainty in an ash transport and dispersal model: application to the Eyjafjallajokull plume of 14-16 April 2010
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DOI：
10.1007/s00445-012-0665-2
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发表时间：
2012-12-01
影响因子：
3.5
通讯作者：
Ripepe, Maurizio
中科院分区：
文献类型：
--
作者：
Bursik, Marcus;Jones, Matthew;Ripepe, Maurizio
2010年4月14日冰岛Eyjafjallajokull火山爆发爆发阶段的源条件数据已被用作基于轨迹的喷发柱模型的输入。这个模型又被用来产生适合作为火山灰运输和扩散模型——puff——输入的输出，该模型被用来在接下来的几天里向欧洲和欧洲上空传播阵发性火山灰云。一些源参数，特别是喷口半径、喷口源速度、喷出物的平均粒度和喷出物粒度的标准偏差，由于我们缺乏对源的确切条件的了解，已经被分配了概率分布。输入变量的这些概率分布已经以蒙特卡罗方式进行了采样，使用的技术产生了我们在此称为多项式混沌正交加权估计（PCQWE）的灰输运和扩散模型的输出参数。PCQWE相对于蒙特卡罗的优势在于，由于它智能地对输入参数空间进行采样，因此需要更少的模型运行来产生输出变量的矩和概率估计。在输入变量的每个样本点上，执行一次模型运行。然后通过对感兴趣的输出参数的加权值适当求和来计算输出力矩和概率。利用计算喷发柱模型，结合从火山口附近收集的无线电探空仪数据得到的已知天气条件，我们可以估计2010年4月14日的初始大规模喷发速率可能高达10(8)kg/s，几乎可以肯定高于10(7)kg/s。这一估计与PCQWE计算的顺风羽流的概率包络线一致。结果进一步表明，统计矩和概率可以通过使用9(4)= 6,561 PCQWE模型运行在合理的时间内计算出来，而不是使用标准蒙特卡罗技术可能需要数百万模型运行。输出的平均灰云高度加上三个标准偏差——包括概率质量的99.7%——与2010年4月16日从Meteosat-9 SEVIRI数据中检索到的灰云四维位置相比较，当时灰云飘过欧洲中北部。最后，计算统计时刻和概率的能力可能允许更好地分离科学和决策，使科学家能够更好地专注于减少错误，决策者能够更好地专注于风险评估的“划清界限”。
Data on source conditions for the 14 April 2010 paroxysmal phase of the Eyjafjallajokull eruption, Iceland, have been used as inputs to a trajectory-based eruption column model, bent. This model has in turn been adapted to generate output suitable as input to the volcanic ash transport and dispersal model, puff, which was used to propagate the paroxysmal ash cloud toward and over Europe over the following days. Some of the source parameters, specifically vent radius, vent source velocity, mean grain size of ejecta, and standard deviation of ejecta grain size have been assigned probability distributions based on our lack of knowledge of exact conditions at the source. These probability distributions for the input variables have been sampled in a Monte Carlo fashion using a technique that yields what we herein call the polynomial chaos quadrature weighted estimate (PCQWE) of output parameters from the ash transport and dispersal model. The advantage of PCQWE over Monte Carlo is that since it intelligently samples the input parameter space, fewer model runs are needed to yield estimates of moments and probabilities for the output variables. At each of these sample points for the input variables, a model run is performed. Output moments and probabilities are then computed by properly summing the weighted values of the output parameters of interest. Use of a computational eruption column model coupled with known weather conditions as given by radiosonde data gathered near the vent allows us to estimate that initial mass eruption rate on 14 April 2010 may have been as high as 10(8) kg/s and was almost certainly above 10(7) kg/s. This estimate is consistent with the probabilistic envelope computed by PCQWE for the downwind plume. The results furthermore show that statistical moments and probabilities can be computed in a reasonable time by using 9(4) = 6,561 PCQWE model runs as opposed to millions of model runs that might be required by standard Monte Carlo techniques. The output mean ash cloud height plus three standard deviations-encompassing c. 99.7 % of the probability mass-compares well with four-dimensional ash cloud position as retrieved from Meteosat-9 SEVIRI data for 16 April 2010 as the ash cloud drifted over north-central Europe. Finally, the ability to compute statistical moments and probabilities may allow for the better separation of science and decision-making, by making it possible for scientists to better focus on error reduction and decision makers to focus on "drawing the line" for risk assessment.