Improved quantitative forecasts of volcanic ash are in great demand by the aviation industry to enable better risk management during disruptive volcanic eruption events. However, poor knowledge of volcanic source parameters and other dispersion and transport modelling uncertainties, such as those due to errors in numerical weather prediction fields, make this problem very challenging. Nonetheless, satellite-based algorithms that retrieve ash properties, such as mass load, effective radius, and cloud top height, combined with inverse modelling techniques, such as ensemble filtering, can significantly ameliorate these problems. The satellite-retrieved data can be used to better constrain the volcanic source parameters, but they can also be used to avoid the description of the volcanic source altogether by direct insertion into the forecasting model. In this study we investigate the utility of the direct insertion approach when employed within an ensemble filtering framework. Ensemble members are formed by initializing dispersion models with data from different timesteps, different values of cloud top height, thickness, and NWP ensemble members. This large ensemble is then filtered with respect to observations to produce a refined forecast. We apply this approach to 14 different eruption case studies in the tropical atmosphere. We demonstrate that the direct insertion of data improves model forecast skill, particularly when it is used in a hybrid ensemble in which some ensemble members are initialized from the volcanic source. Moreover, good forecast skill can be obtained even when detailed satellite retrievals are not available, which is frequently the case for volcanic eruptions in the tropics.

中文翻译：

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通过直接插入卫星数据和集合过滤改进集合火山灰预测

航空业迫切需要改进火山灰的定量预测，以便在破坏性火山喷发事件期间实现更好的风险管理。然而，对火山源参数和其他弥散和传输建模不确定性的了解不足，例如由于数值天气预报领域中的错误而导致的不确定性，使这个问题变得非常具有挑战性。尽管如此，基于卫星的算法可以检索灰烬属性，如质量载荷、有效半径和云顶高度，结合逆建模技术，如集合滤波，可以显着改善这些问题。卫星检索数据可用于更好地约束火山源参数，但它们也可用于通过直接插入预测模型来完全避免对火山源的描述。在这项研究中，我们调查了在集成过滤框架中使用时直接插入方法的效用。集合成员是通过使用来自不同时间步长、云顶高度、厚度和 NWP 集合成员的不同值的数据初始化色散模型而形成的。然后根据观测值对这个大型集合进行过滤，以产生精确的预测。我们将这种方法应用于热带大气中 14 个不同的喷发案例研究。我们证明数据的直接插入提高了模型预测技能，特别是当它用于混合集合时，其中一些集合成员从火山源初始化。此外，即使没有详细的卫星检索，也可以获得良好的预报技巧，