Estimation of optimal dispersion model source parameters using satellite detections of volcanic ash

In this paper we demonstrate how parameters describing the geometry of the volcanic ash source for a particular volcanic ash dispersion model (Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT)) may be inferred by the use of satellite data and multiple trial simulations. The areas of space likely to be contaminated by ash are identified with the aid of various remote sensing techniques, and polygons are drawn around these areas as they would be in an operational setting. Dispersion model simulations are initialized by either a cylindrical source or a specified ash distribution depending on the context. Parameters of interest such as the base and top height, diameter, and optimal release time of the cylindrical source or the height of the specified ash distribution are inferred by forming a parameter grid and running multiple simulations for each parameter grid point value. Optimal values of the parameter values are identified by calculating spatial correlations between the model simulations and observations. We demonstrate that the methodology can be used to correctly infer various model parameters and improve volcanic ash forecasts in various eruption case studies. Plain Language Summary Airborne volcanic ash is a hazard to aviation. Accurate forecasts of airspace likely to be contaminated by ash are therefore crucial for effective risk management. In this paper we show how information about the location of ash‐contaminated areas at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the height of the ash column at the volcano. This in turn leads to better forecasts of ash transport. We demonstrate the efficacy of this approach using several case studies. Key Points Ash detection fields constructed from a variety of sources, including human input, are a reliable source of information about volcanic ash Ash detections can be used to infer important dispersion model parameters such as ash column height Use of optimal dispersion model parameters as obtained from the detection field leads to better forecasts and therefore improved guidance