Volcanic ash forecast using ensemble-based data assimilation: An Ensemble Transform Kalman Filter coupled with FALL3D-7.2 model (ETKF-FALL3D, version 1.0)

Quantitative volcanic ash cloud forecasts are prone to uncertainties coming from the source term quantification (e.g. eruptionstrength or vertical distribution of the emitted particles), with consequent implications on operational ash impact assessment.We present an ensemble-based data assimilation and forecast system for volcanic ash dispersal and deposition aimed at reducinguncertainties related to eruption source parameters. The FALL3D atmospheric dispersal model is coupled with the Ensemble5Transform Kalman Filter (ETKF) data assimilation technique by combining ash mass loading observations with ash dispersalsimulations in order to obtain a better joint estimation of 3D ash concentration and source parameters. The ETKF-FALL3Ddata assimilation system is evaluated performing Observation System Simulation Experiments (OSSE) in which syntheticobservations of fine ash mass loadings are assimilated. The evaluation of the ETKF-FALL3D system considering referencestates of steady and time-varying eruption source parameters shows that the assimilation process gives both better estimations10of ash concentration and time-dependent optimized values of eruption source parameters. The joint estimation of concentrationsand source parameters leads to a better analysis and forecast of the 3D ash concentrations. Results show the potential of themethodology to improve volcanic ash cloud forecasts in operational contexts.