Model sensitivities in the case of high-resolution Eulerian simulations of local tephra transport and deposition

Abstract

Tephra transport modelling has predominantly been carried out over synoptic-scale resolutions, resolving only fundamental atmospheric circulations. Consequently, model sensitivity studies have focused on these resolutions. In recent years, improved eruption source parameter (ESP) estimation methods and computational efficiency have progressively led to the use of higher resolutions to increase the fidelity of tephra transport simulations. This is a new computational regime whose model sensitivities remain largely unexplored. Here, high-resolution simulations using a 300 m horizontal grid spacing were carried out using WRF and FALL3D to assess sensitivity to: meteorological input data (using both reanalysis and forecast data), boundary layer parametrisation, plume model, settling velocity, total grain-size distribution (TGSD), and aggregation, leading to a total of 1280 possible configurations. An eruption that occurred on 28 July 2019 at Sakurajima volcano, Japan, is used as a case study, as the northwards dispersal of a 3.8 km-high plume led to the temporary closure of Kagoshima airport. Sedimentation was observed at three optical disdrometers on the volcano, providing insight on the deposition timing and particle size distribution. Results reveal that meteorological input data can have a significant impact over the fidelity of the simulations, with errors changing over one order of magnitude depending on the initialisation dataset. This was found to be the result of local bias in the simulated wind fields over the region of dispersal, which was not represented in the domain-wide assessment of the WRF simulations. Ensemble simulations using different meteorological input data were found to mitigate this, significantly increasing the fidelity of the final ensemble product. Use of a plume model and the correct estimation of the TGSD were seen to significantly improve results. Finally, even with detailed data to inform the choice, use of an empirical aggregation scheme was seen to significantly degrade the accuracy of the simulations.