Variational data assimilation of satellite observations to estimate volcanic ash emissions

Volcanic eruptions release a large amount of volcanic ash, which can pose hazard to human and animal health, land transportation, and aviation safety. Volcanic Ash Transport and Dispersion (VATD) models are critical tools to provide advisory information and timely volcanic ash forecasts. Due to the complexity and the uncertainty of many dynamic processes involved in the volcanic ash distribution, even the most advanced VATDs today are not capable to reproduce the reality accurately. It is necessary to integrate available observations in the models for more accurate predictions by employing data assimilation techniques.In addition to a valid VATD, ash emissions, usually used as input so the model, are crucial for the forecasts of the locations and shapes of the ash cloud. In general, the eruption source parameters for the construction of the emission are poorly known, which include Plume Height (PH), Mass Eruption Rate (MER) and vertical distribution of the emission rate. Even when PH can be obtained from ground-based observations in some cases, the emission source computed from this PH and a MER empirically related to this PH remains highly uncertain. Not to mention the volcanoes which are unmonitored or hardly accessible, the PH can merely be retrieved from satellite data with a large uncertainty and temporal insufficiency. Fortunately, satellite instruments are able to observe the movement of an ash cloud with a global coverage. Therefore, this thesis focuses on the estimation of the volcanic ash emissions by assimilating Ash Mass Loadings (AMLs) retrieved from satellite data to improve the accuracy of forecasts. Among all available data assimilation approaches, Four Dimensional Variational assimilation (4D-Var) approach was chosen as a suitable one. 4D-Var seeks an optimal set of parameters, including model states, initial conditions and systematic parameters, by minimizing a cost function which combines the model simulations and observations over a period accord
Mathematical Physics