Abstracts Category : Other

Variational data assimilation of satellite observations to estimate volcanic ash emissions

by S Lu

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 according to their statistic properties. 4D-Var with a standard form of the cost function is tested in a twin experiment framework, where synthetic observations of ash columns computed from model simulated 3D ash concentrations are used. The results show that Standard 4DVar (Std4DVar) is unable to reconstruct the vertical profile of the emission. The injection layer containing the maximal amount of emission rate cannot be accurately determined. This failure is attributed to the fact that AML data lacks vertical resolution. Using the AMLs, it is difficult to reconstruct the volcanic ash emission presented in forms of an eruption column.To deal with this problem, a Trajectory-based 4D-Var (Trj4DVar) approach is proposed. Trj4DVar reformulates the cost function in a regression type which computes the total difference between observed ash columns and a linear combination of simulated trajectories coupled with a priori emission knowledge. The results of twin experiments show that, for most cases, Trj4DVar is capable of estimating the input emission column when aAdvisors/Committee Members: Lin, H.X., Heemink, A.W..