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Testing the accuracy of a 1-D volcanic plume model in estimating mass eruption rate

机译:测试一维火山羽模型在估计火山喷发率中的准确性

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During volcanic eruptions, empirical relationships are used to estimate mass eruption rate from plume height. Although simple, such relationships can be inaccurate and can underestimate rates in windy conditions. One-dimensional plume models can incorporate atmospheric conditions and give potentially more accurate estimates. Here I present a 1-D model for plumes in crosswind and simulate 25 historical eruptions where plume height H_(obs) was well observed and mass eruption rate M_(obs) could be calculated from mapped deposit mass and observed duration. The simulations considered wind, temperature, and phase changes of water. Atmospheric conditions were obtained from the National Center for Atmospheric Research Reanalysis 2.5° model. Simulations calculate the minimum, maximum, and average values (M_(min), M_(max), and M_(avg)) that fit the plume height. Eruption rates were also estimated from the empirical formula M_(empir) = 140H_(obs) 4.14 (M_(empir) is in kilogram per second, Hobs is in kilometer). For these eruptions, the standard error of the residual in log space is about 0.53 for Mavg and 0.50 for Mempir. Thus, for this data set, the model is slightly less accurate at predicting M_(obs) than the empirical curve. The inability of this model to improve eruption rate estimates may lie in the limited accuracy of even well-observed plume heights, inaccurate model formulation, or the fact that most eruptions examined were not highly influenced by wind. For the low, wind-blown plume of 14–18 April 2010 at Eyjafjallaj?kull, where an accurate plume height time series is available, modeled rates do agree better with M_(obs) than M_(empir).
机译:在火山喷发过程中,经验关系用于根据羽流高度估算喷发速率。尽管简单,但这种关系可能不准确,并且可能在大风条件下低估了比率。一维羽状模型可以结合大气条件,并可能提供更准确的估计。在这里,我为侧风中的羽流提供了一维模型,并模拟了25次历史喷发,在这些历史喷发中,可以很好地观察到羽流高度H_(obs),并且可以根据映射的沉积物质量和观测到的持续时间来计算喷发速率M_(obs)。模拟考虑了水的风,温度和相变。大气条件是从国家大气研究再分析中心2.5°模型获得的。仿真将计算适合羽流高度的最小值,最大值和平均值(M_(min),M_(max)和M_(avg))。还根据经验公式M_(empir)= 140H_(obs)4.14(M_(empir)以千克每秒为单位,Hobs以公里为单位)估算爆发速率。对于这些喷发,Mavg和Mempir的对数空间中残差的标准误差约为0.53。因此,对于该数据集,该模型在预测M_(obs)时的准确性略低于经验曲线。该模型无法提高喷发率估计值的原因可能在于,即使良好观察到的羽流高度的准确性有限,模型公式不正确,或者检查的大多数喷发不受风的影响很大。对于2010年4月14日至18日在Eyjafjallaj?kull的低风吹羽流(可提供准确的羽流高度时间序列),模型化速率与M_(obs)确实比M_(empir)更好。

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