Abstract

Abstract. This study focuses on two new aspects of inverse modelling of volcanic emissions. First, we derive an observation operator for satellite retrievals of plume height, and second, we solve the inverse problem using an algorithm based on the 4D-Var data assimilation method. The approach is first tested in a twin experiment with simulated observations and further evaluated by assimilating IASI SO2 plume height and total column retrievals in a source term inversion for the 2010 eruption of Eyjafjallajökull. The inversion resulted in temporal and vertical reconstruction of the SO2 emissions during 1–20 May 2010 with formal vertical and temporal resolutions of 500 m and 12 h.The plume height observation operator is based on simultaneous assimilation of the plume height and total column retrievals. The plume height is taken to represent the vertical centre of mass, which is transformed into the first moment of mass (centre of mass times total mass). This makes the observation operator linear and simple to implement. The necessary modifications to the observation error covariance matrix are derived.Regularization by truncated iteration is investigated as a simple and efficient regularization method for the 4D-Var-based inversion. In the twin experiments, the truncated iteration was found to perform similarly to the commonly used Tikhonov regularization, which in turn is equivalent to a Gaussian a priori source term. However, the truncated iteration allows the level of regularization to be determined a posteriori without repeating the inversion.In the twin experiments, assimilating the plume height retrievals resulted in a 5–20 % improvement in root mean squared error but simultaneously introduced a 10–20 % low bias on the total emission depending on assumed emission profile. The results are consistent with those obtained with real data. For Eyjafjallajökull, comparisons with observations showed that assimilating the plume height retrievals reduced the overestimation of injection height during individual periods of 1–3 days, but for most of the simulated 20 days, the injection height was constrained by meteorological conditions, and assimilation of the plume height retrievals had only small impact. The a posteriori source term for Eyjafjallajökull consisted of 0.29 Tg (with total column and plume height retrievals) or 0.33 Tg (with total column retrievals only) erupted SO2 of which 95 % was injected below 11 or 12 km, respectively.

Highlights

  • Sulfur dioxide (SO2) is one of the major gas-phase species released in volcanic eruptions

  • The comparison with the Infrared Atmospheric Sounding Interferometer (IASI) retrievals, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data and weather radar observations of the plume shows that the resulting vertical distributions were frequently in good agreement with the observations even if only total column retrievals were used in the inversion

  • The approach was tested by performing a source term inversion using both artificial data and the SO2 retrievals from the IASI instrument during the Eyjafjallajökull eruption in May 2010

Read more

Summary

Introduction

Sulfur dioxide (SO2) is one of the major gas-phase species released in volcanic eruptions. Volcanic SO2 plumes can be detected by satellite instruments measuring in either ultraviolet (UV) or infrared (IR) wavelengths; reliably forecasting the atmospheric transport of volcanic plumes is hindered by the lack of in situ measurements to characterize the emission fluxes of volcanic species (Carn et al, 2009; Stohl et al, 2011; Zehner, 2012). While methods based purely on satellite retrievals (Theys et al, 2013, and references therein) exist for inferring the total SO2 flux for a given eruption, a successful prediction of volcanic tracers generally requires information on the vertical profile of emissions. An important technique for assessing both vertical and temporal distribution of the emission fluxes is provided by inverse dispersion modelling, first demonstrated for volcanic emissions by Eckhardt et al (2008)

Objectives
Methods
Findings
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call