Satellite data assimilation to improve forecasts of volcanic ash concentrations.

Data assimilation is a powerful tool that requires available observations to improve model forecast accuracy. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations into the assimilation scheme. However, these satellite-retrieved data are often two-dimensional (2D), and cannot be easily combined with a three-dimensional (3D) volcanic ash model to continuously improve the volcanic ash state in a data assimilation system. 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 continuously 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. It is shown that an effective half-day ash forecast significantly improves the quality of the advice given to aviation over continental Europe. [ABSTRACT FROM AUTHOR]