Chapter 38
Ensemble-Based Data Assimilation
and Forecasting of Volcanic Ash
Andreas Uppstu, Julius Vira and Mikhail Sofiev
Abstract Volcanic ash and other aerosols such as desert dust form significant hazards for aviation and can cause both direct safety threats and significant economic
losses. However, forecasts of aviation hazards have generally been deterministic,
although the available computational resources would easily allow for them to be
ensemble-based. In principle, ensemble-based forecasts can enable more accurate
error estimates and thus an improved risk management framework. Advanced data
assimilation methods, such the Ensemble Kalman Filter, coupled with a meteorological forecast ensemble, provide increased accuracy and the possibility to estimate the source term by taking into account its correlation with the observed ash
concentration.
38.1 Introduction
Real-time data assimilation of observations of volcanic ash has potential both to
improve flight safety and reduce the aviation related costs of volcanic eruptions.
Within the EUNADICS-AV project, ensemble-based forecasting is suggested as the
base for future products aimed to improve flight safety and to optimize flight routing
and scheduling. In addition to volcanic eruptions, hazards included in the project
are releases of radioactive matter, dust storms and vegetation fires. The Finnish
Meteorological Institute utilizes the chemical transport model SILAM [3], coupled
with the Ensemble Kalman Filter (EnKF), which is a data-assimilation method naturally suited for ensemble-based forecasting, to assimilate satellite- and ground-based
A. Uppstu (B) · J. Vira · M. Sofiev
Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland
e-mail: Andreas.Uppstu@fmi.fi
M. Sofiev
e-mail: Mikhail.Sofiev@fmi.fi
J. Vira
Cornell University, Ithaca, NY, USA
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_38
243
Ensemble-Based Data Assimilation
and Forecasting of Volcanic Ash
Andreas Uppstu, Julius Vira and Mikhail Sofiev
Abstract Volcanic ash and other aerosols such as desert dust form significant hazards for aviation and can cause both direct safety threats and significant economic
losses. However, forecasts of aviation hazards have generally been deterministic,
although the available computational resources would easily allow for them to be
ensemble-based. In principle, ensemble-based forecasts can enable more accurate
error estimates and thus an improved risk management framework. Advanced data
assimilation methods, such the Ensemble Kalman Filter, coupled with a meteorological forecast ensemble, provide increased accuracy and the possibility to estimate the source term by taking into account its correlation with the observed ash
concentration.
38.1 Introduction
Real-time data assimilation of observations of volcanic ash has potential both to
improve flight safety and reduce the aviation related costs of volcanic eruptions.
Within the EUNADICS-AV project, ensemble-based forecasting is suggested as the
base for future products aimed to improve flight safety and to optimize flight routing
and scheduling. In addition to volcanic eruptions, hazards included in the project
are releases of radioactive matter, dust storms and vegetation fires. The Finnish
Meteorological Institute utilizes the chemical transport model SILAM [3], coupled
with the Ensemble Kalman Filter (EnKF), which is a data-assimilation method naturally suited for ensemble-based forecasting, to assimilate satellite- and ground-based
A. Uppstu (B) · J. Vira · M. Sofiev
Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, Finland
e-mail: Andreas.Uppstu@fmi.fi
M. Sofiev
e-mail: Mikhail.Sofiev@fmi.fi
J. Vira
Cornell University, Ithaca, NY, USA
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_38
243