Preprints
https://doi.org/10.5194/acp-2021-747
https://doi.org/10.5194/acp-2021-747

  21 Sep 2021

21 Sep 2021

Review status: this preprint is currently under review for the journal ACP.

Data Assimilation of Volcanic Aerosols using FALL3D+PDAF

Leonardo Mingari1, Arnau Folch2, Andrew T. Prata3, Federica Pardini4, Giovanni Macedonio5, and Antonio Costa6 Leonardo Mingari et al.
  • 1Barcelona Supercomputing Center, Barcelona, Spain
  • 2Geociencias Barcelona (GEO3BCN-CSIC), Barcelona, Spain
  • 3Sub-department of Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, U.K.
  • 4Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy
  • 5Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Naples, Italy
  • 6Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy

Abstract. Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on infrastructures, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height, physical properties of particles or meteorological fields, represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based Data Assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and, (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the Local Ensemble Transform Kalman Filter (LETKF) is used to assimilate satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.

Leonardo Mingari et al.

Status: open (until 02 Nov 2021)

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Leonardo Mingari et al.

Leonardo Mingari et al.

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Short summary
We present a new implementation of an ensemble-based data assimilation method to improve forecasting of volcanic aerosols. This system can be efficiently integrated into operational workflows by exploiting high-performance computing resources. We found a dramatic improvement of forecast quality when satellite retrievals are continuously assimilated. Management of volcanic risk and reduction of aviation impacts can strongly benefit from this research.
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