Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements

Abstract

Abstract. The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of 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 our approach, we use real-life aircraft in situ observations, measured in the northwestern part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system combined with a volcanic ash transport model 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 through assimilating real-life in situ measurements. After a continuous assimilation, it is shown that the aviation advice for Germany, the Netherlands and Luxembourg can be significantly 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 greatly improved.

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: a case study with the LOTOS-EUROS model (version 1.10)

“…The measurement route and values are demonstrated in Fig. 1b, c and the details are described in Weber et al (2012) and Fu et al (2016). The observational network at time t k is defined by the operator H k which maps the state vector x to the observational vector y by…”

“…The LOTOS-EUROS model (Schaap et al, 2008) is an operational model focusing on nitrogen oxides, ozone, particulate matter, and volcanic ash. The model configurations for volcanic ash were discussed in detail by Fu et al (2016). For volcanic ash simulation, the model is configured with a state vector of size 180 × 200 × 18 × 6 (the dimensions correspond to longitude, latitude, vertical level, and ash species), and the size of the model state is thus calculated as ∼ 10 6 .…”

“…Since then, research on fast and accurate volcanic ash fore-casts has gained much attention, because it is needed to provide timely and accurate aviation advice for frequently operated commercial airplanes. It was shown that the accuracy of volcanic ash transport can be significantly improved by the DA system in Fu et al (2016). Therefore, it is urgent to also consider the computational aspect, i.e., improving the computational speed of the volcanic ash DA system as quickly as possible.…”

“…Using data assimilation (DA) to improve model forecast accuracy is a powerful approach (Lu et al, 2016a). Recently, ensemble-based DA (Evensen, 2003) has been evaluated as very useful for improving volcanic ash forecasts and regional aviation advice (Fu et al, 2016). It corrects volcanic ash concentrations by continuously assimilating observations.…”

“…It corrects volcanic ash concentrations by continuously assimilating observations. In Fu et al (2016), real aircraft in situ measurements were assimilated using the ensemble Kalman filter (EnKF), which is the best known and most popular ensemble-based DA method. Based on the validation with independent data, ensemble-based DA was concluded as being powerful for improving the forecast accuracy.…”