A Computationally Efficient Ensemble Filtering Scheme for Quantitative Volcanic Ash Forecasts

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. Plain Language Summary Airborne volcanic ash is a potentially catastrophic hazard to flying aircraft. Therefore, aircraft must be diverted around ash plumes or grounded, affecting the public at large and causing significant economic impacts to the aviation sector. Improved ash forecasting models that provide quantitative information about the amounts of ash within an ash cloud and the associated uncertainties are therefore in great demand by the aviation industry. The amounts of ash within ash clouds may be estimated by a new generation of satellite‐based algorithms. A new method of assimilating these satellite‐derived data, both from past eruptions and in real‐time, into quantitative volcanic ash forecast models is presented in this study. The method is computationally efficient and applicable to a wide range of operational contexts, including situations where only the ash cloud boundaries are known. It is shown that the method leads to improved forecasts in several eruption case studies. Key Points Assimilation of satellite data by ensemble filtering improves forecasts of volcanic ash Filter degeneracy may be overcome by c