Abstract

Abstract. This paper presents FALL3D-8.0, the last version release of an open-source code with 15+ years of track record and a growing number of users in the volcanological and atmospheric communities. The code has been redesigned and rewritten from scratch in the framework of the EU Centre of Excellence for Exascale in Solid Earth (ChEESE) in order to overcome legacy issues and allow for successive optimisations in the preparation of the code towards extreme-scale computing. However, this baseline version already contains substantial improvements in terms of model physics, solving algorithms, and code accuracy and performance. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. The solving strategy has also been changed, replacing the former central-difference scheme for a high-resolution central-upwind scheme derived from finite volumes, which minimises numerical diffusion even in the presence of sharp concentration gradients and discontinuities. The parallelisation strategy, input/output (I/O), model pre-process workflows and memory management have also been reconsidered, leading to substantial improvements on code scalability, efficiency and overall capability to handle much larger problems. All these new features and improvements have implications on operational model performance and allow, among others, adding data assimilation and ensemble forecast in future releases. This paper details the FALL3D-8.0 model physics and the numerical implementation of the code.

Highlights

  • FALL3D is an open-source offline Eulerian model for atmospheric passive transport and deposition based on the socalled advection–diffusion–sedimentation (ADS) equation

  • Code improvements at different levels have been continuously incorporated since including relevant milestones leading to code version upgrades, e.g. the coupling with 1-D buoyant plume theory (BPT) models to define eruption column sources (v2.x, 2004), the introduction of the Lax–Wendroff (LW) centraldifference scheme for solving the ADS equation (v3.x, 2005) and other algorithmic improvements (Costa et al, 2006), full code rewriting in Fortran 90 and distributed memory parallelisation by means of Message Passing Interface (MPI) (v5.x, 2007), first implementations of operational workflows to forecast ash cloud dispersal and fallout (Folch et al, 2008, 2009), and several other improvements until the v7.3.4 release in 2018

  • After 15+ years, the atmospheric transport model FALL3D has been completely rewritten and modernised to overcome legacy constraints in the former release (v7.x) that precluded the introduction of new functionalities and seriously limited the scalability and performance of the code on hundreds or thousands of processors

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Summary

Introduction

FALL3D is an open-source offline Eulerian model for atmospheric passive transport and deposition based on the socalled advection–diffusion–sedimentation (ADS) equation. Code improvements at different levels have been continuously incorporated since including relevant milestones leading to code version upgrades, e.g. the coupling with 1-D buoyant plume theory (BPT) models to define eruption column sources (v2.x, 2004), the introduction of the Lax–Wendroff (LW) centraldifference scheme for solving the ADS equation (v3.x, 2005) and other algorithmic improvements (Costa et al, 2006), full code rewriting in Fortran 90 and distributed memory parallelisation by means of Message Passing Interface (MPI) (v5.x, 2007), first implementations of operational workflows to forecast ash cloud dispersal and fallout (Folch et al, 2008, 2009), and several other improvements (e.g. de la Cruz et al, 2016) until the v7.3.4 release in 2018 Along these 15+ years, FALL3D has been used in multiple applications These will require of teams of processors associated with different ensemble members or model grids, respectively

Model governing equations
D Df d dA dn ds F FV FH fi G Gh Gm g H H HT h hc hp
Diffusion tensor
Sedimentation velocity
Emissions
Deposition mechanisms
Gravity spreading of the umbrella region
Aggregation
Radioactive decay
Data insertion
Coordinate mappings and scaling
Discretisation and solving algorithm
Algorithm benchmarks
Model execution workflow
Parallelisation and performance
Findings
Conclusions
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