Evaluating a meteorological downscaling method for volcanic ash dispersion and deposition modelling

High-resolution meteorological data is essential for accurate volcanic ash dispersion modelling, particularly in regions with complex topography. However, performing fully dynamical atmospheric simulations at very fine spatial resolution is computationally expensive and may limit their applicability in contexts where urgent computing is required, such as operation forecasting. Diagnostic downscaling methods offer a potential alternative by enhancing coarse-resolution meteorological fields at a lower computational cost, but their added value relative to full dynamical nesting remains to be further explored. In this work, we assess the effectiveness of diagnostic meteorological downscaling using an integrated simulation workflow based on the MetPrep tool coupled with the FALL3D ash dispersion model. This approach is applied to the case study of the 2021 Tajogaite eruption (La Palma), comparing meteorological data from three WRF-ARW dynamically nested domains with increasing spatial and temporal resolution (domains d01, d02 and d03) against diagnostic downscaling applied to the coarser WRF domains (d01+MetPrep and d02+MetPrep). All dispersion simulations are run using identical eruptive parameters in order to isolate the impact of the meteorological downscaling method. The simulated ash deposits are compared against field observations using point-to-point validation metrics and spatial characterisation based on isopach area fits. In addition, physically motivated wind metrics, including vertical wind shear and wind-topography coherence, are analysed to interpret the effects introduced by diagnostic downscaling on the flow. Preliminary results show that diagnostic downscaling can partially bridge the gap between coarse and high-resolution dynamical simulations, improving the representation of near-surface flow and ash deposition patterns at a fraction of the computational cost. The study highlights both the potential and the limitations of diagnostic downscaling as an alternative to full dynamical nesting for volcanic ash dispersion applications.

Funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement No 101093038, ChEESE-2P, project PCI2022-134973-2 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.