Estimation of volcanic ash emissions using trajectory-based 4D-Var data assimilation

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using available observations through data assimilation could help to increase the accuracy of forecasts and provide reliable advisory information. This paper focuses on the use of satellite total-ash-column data in 4D-Var based assimilations. Experiments show that it is very difficult to estimate the vertical distribution of effective volcanic ash injection rates from satellite-observed ash columns using a standard 4D-Var assimilation approach. This paper addresses the ill-posed nature of the assimilation problem from the perspective of a spurious relationship. To reduce the influence of a spurious relationship created by a radiate observation operator, an adjoint-free trajectory-based 4D-Var assimilation method is proposed, which is more accurate to estimate the vertical profile of volcanic ash from volcanic eruptions. The method seeks the optimal vertical distribution of emission rates of a reformulated cost function that computes the total difference between simulated and observed ash columns. A 3D simplified aerosol transport model and synthetic satellite observations are used to compare the results of both the standard method and the new method.

[1]  Sha Lu,et al.  Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport , 2015 .

[2]  M. Kahnert,et al.  The EMEP data assimilation system: Technical description and first results , 2010 .

[3]  O. Talagrand,et al.  4D-variational data assimilation with an adjoint air quality model for emission analysis , 2000, Environ. Model. Softw..

[4]  Alfred J Prata,et al.  Infrared radiative transfer calculations for volcanic ash clouds , 1989 .

[5]  A. Stohl,et al.  Volcanic dust characterization by EARLINET during Etna's eruptions in 2001–2002 , 2008 .

[6]  A. Doicu,et al.  Numerical Regularization for Atmospheric Inverse Problems , 2010 .

[7]  Peter W. Webley,et al.  Analyzing the Eyjafjallajökull 2010 eruption using satellite remote sensing, lidar and WRF‐Chem dispersion and tracking model , 2012 .

[8]  David J. Schneider,et al.  Observations of Volcanic Clouds in Their First Few Days of Atmospheric Residence: The 1992 Eruptions of Crater Peak, Mount Spurr Volcano, Alaska , 2001, The Journal of Geology.

[9]  Xiangjun Tian,et al.  A POD‐based ensemble four‐dimensional variational assimilation method , 2011 .

[10]  Xiangjun Tian,et al.  An ensemble-based explicit four-dimensional variational assimilation method , 2008 .

[11]  Peter W. Webley,et al.  Inclusion of ash and SO 2 emissions from volcanic eruptions in WRF-Chem: development and some applications , 2012 .

[12]  P. Baxter,et al.  The respiratory health hazards of volcanic ash: a review for volcanic risk mitigation , 2006 .

[13]  Lothar Reichel,et al.  SIMPLE SQUARE SMOOTHING REGULARIZATION OPERATORS , 2009 .

[14]  Peter N. Francis,et al.  Simulated volcanic ash imagery: A method to compare NAME ash concentration forecasts with SEVIRI imagery for the Eyjafjallajökull eruption in 2010 , 2012 .

[15]  F. L. Dimet,et al.  Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .

[16]  Hendrik Elbern,et al.  Emission rate and chemical state estimation by 4-dimensional variational inversion , 2007 .

[17]  Larry G. Mastin,et al.  A multidisciplinary effort to assign realistic source parameters to models of volcanic ash-cloud transport and dispersion during eruptions , 2009 .

[18]  Q. Xiao,et al.  An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW) , 2009 .

[19]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[20]  M. Coffey,et al.  Airborne observations of SO2, HCl, and O3 in the stratospheric plume of the Pinatubo Volcano in July 1991 , 1992 .

[21]  J. Flemming,et al.  Volcanic sulfur dioxide plume forecasts based on UV satellite retrievals for the 2011 Grímsvötn and the 2010 Eyjafjallajökull eruption , 2013 .

[22]  K. Dean,et al.  PUFF: A high-resolution volcanic ash tracking model , 1998 .

[23]  T. Casadevall,et al.  The 1989–1990 eruption of Redoubt Volcano, Alaska: impacts on aircraft operations , 1994 .

[24]  William I. Rose,et al.  Retrieval of sizes and total masses of particles in volcanic clouds using AVHRR bands 4 and 5 , 1994 .

[25]  A. Robock.  Volcanic eruptions and climate , 2000 .

[26]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. Ii: Numerical Results , 2007 .

[27]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[28]  Peter Bergamaschi,et al.  Four‐dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations , 2008 .

[29]  Albert Ansmann,et al.  Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations , 2011 .

[30]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[31]  J. Schmetz,et al.  AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG) , 2002 .

[32]  Hendrik Elbern,et al.  Four-Dimensional Variational Assimilation of Atmospheric Chemical Data - Application to Regional Modelling of Air Quality , 2009, LSSC.