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A Computationally Efficient Ensemble Filtering Scheme for Quantitative Volcanic Ash Forecasts
Author(s) -
Zidikheri Meelis J.,
Lucas Christopher
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd033094
Subject(s) - volcanic ash , filter (signal processing) , computer science , ensemble forecasting , sampling (signal processing) , satellite , ensemble kalman filter , term (time) , meteorology , algorithm , volcano , kalman filter , geology , artificial intelligence , engineering , extended kalman filter , physics , quantum mechanics , seismology , computer vision , aerospace engineering
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.