A method of assimilating satellite observations in quantitative ensemble forecasting models of airborne volcanic ash is presented in this study. The method employs many trial dispersion model simulations that are generated by both deterministic and random perturbations of the source term and use of an ensemble of numerical weather prediction model fields. An ensemble filter is then applied to the trial simulations, which are either selected or rejected by the filter based on their degree of agreement with observations within a specified time window. The observations may be in the form of quantitative satellite retrieved mass load fields or qualitative ash detection fields, which means that useful results can be obtained even when retrievals are not available in real time provided that the ash boundaries can be identified. The filtering process is repeated several times with different random realizations of the source term to reduce sampling error and minimize filter degeneracy, a phenomenon that plagues all ensemble filter models. The selected members are then propagated forward in time beyond the observational time window to form the forecast ensemble. We show, using several eruption case studies, that forecast ensembles constructed in this way are generally superior in skill to reference forecasts that do not assimilate observations.

中文翻译：

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计算火山灰定量预报的高效集合过滤方案

本文提出了一种在机载火山灰定量集合预报模型中吸收卫星观测资料的方法。该方法采用了许多试验弥散模型模拟，这些模拟是通过对源项进行确定性和随机扰动以及使用一组数值天气预报模型字段生成的。然后将集合过滤器应用于试验模拟，根据它们与指定时间窗口内的观测值的一致程度，选择或拒绝过滤器。这些观测结果可以采用定量卫星检索的质量载荷场或定性灰分检测场的形式，这意味着即使无法实时检索，只要可以确定灰分边界，就可以获得有用的结果。使用源项的不同随机实现将过滤过程重复几次，以减少采样误差并使过滤器退化最小化，这种现象困扰着所有集成过滤器模型。然后，选定的成员将在时间上超出观察时间窗口向前传播，以形成预测集合。我们通过几个喷发案例研究表明，以这种方式构建的预报集合通常在技术水平上优于不吸收观测值的参考预报。