Improving volcanic ash forecasts with ensemble-based data assimilation

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The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash forecasting in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs to be run with Eruption Source Parameters (ESPs) such as plume height and mass eruption rate as input, and with data assimilation techniques to continuously improve the forecast. Reliable and accurate ash measurements are crucial for providing successful ash clouds advices. In the first
phase of this research work, simulated aircraft-based volcanic ash measurements, will be assimilated into a transport model to identify the potential benefit of this kind of observations in an assimilation system. The results show that assimilating aircraft-based measurements can improve the state of ash clouds, and can provide an improved forecast. We also show that for an advice on the aeroplane flying level, aircraft-based measurements should preferably be taken at this level. Furthermore
it is shown that in order to make an acceptable advice for aviation decision makers, accurate knowledge about uncertainties of ESPs and measurements is of great importance.

The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruptions. However, because the distal part of a volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In this thesis, we use real-life aircraft in situ observations, measured in the North-West part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced by assimilating real-life in situ measurements. After assimilation, it is shown that the model-based aviation advice for Germany, the Netherlands and Luxembourg can be improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be improved significantly.

Among the data assimilation approaches, the ensemble Kalman filter (EnKF) is a well-known and popular method. A proper covariance localization strategy in the analysis step of EnKF is essential for reducing spurious covariances caused by the finite ensemble size, as shown for this application for assimilation of aircraft in situ measurements. After analyzing the characteristics of the physical forecast error covariances, we present a two-way tracking approach to define the localization matrix
for covariance localization. The result shows that the Two-way-tracking Localized EnKF (TL-EnKF) effectively maintains the correctly specified physical covariances and largely reduces the spurious ones. The computational cost of TL-EnKF is also evaluated and is shown to be advantageous for both serial and parallel implementations. Compared to the commonly used distance-based covariance localization, the two-way tracking approach is shown to be more suitable. In addition, the covariance inflation approach is verified as an additional improvement to TL-EnKF to achieve more accurate results.

A timely prediction requires that the computations of the data assimilation system can be performed quickly (at least than the Wall-clock). We therefore investigate strategies for accelerating the data assimilation algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study on the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash data assimilation is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based data assimilation framework. Moreover, ensemble-based data assimilation with the mask-state algorithm is promising and flexible, because it implements exactly the standard data assimilation without any approximation and it realizes the satisfying performance without any change of the full model.

Infrared satellite measurements of volcanic ash mass loadings are often used as
input observations for the assimilation scheme. However, these satellite-retrieved
data are often two-dimensional (2D), and cannot easily be combined with a three-dimensional (3D) volcanic ash model to improve the volcanic ash state. By integrating available data including ash mass loadings, cloud top heights and thickness information, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2D volcanic ash mass loadings to 3D concentrations at the top layer of the ash cloud. Ensemble-based data assimilation is used to assimilate the extracted measurements of ash concentrations. The results show that satellite data assimilation can force the volcanic ash state to match the satellite observations, and that it improves the forecast of the ash state. Comparison with highly accurate aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts is about a half day.