NH6.9

Modelling atmospheric dispersion and deposition of volcanic ash is becoming increasingly valuable for understanding the potential impacts of explosive volcanic eruptions on infrastructures, air quality and aviation. The generation of high-resolution forecasts depends on the accuracy and reliability of the input data for models. Uncertainties in key parameters such as eruption column height injection, physical properties of particles or meteorological fields, represent a major source of error in forecasting airborne volcanic ash. The availability of nearly real time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context. Data assimilation (DA) is one of the most effective ways to reduce the error associated with the forecasts through the incorporation of available observations into numerical models. Here we present a new implementation of an ensemble-based data assimilation system based on the coupling between the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The implementation is based on the last version release of FALL3D (versions 8.x) tailored to the extreme-scale computing requirements, which has been redesigned and rewritten from scratch in the framework of the EU Center of Excellence for Exascale in Solid Earth (ChEESE). The proposed methodology can be efficiently implemented in an operational environment by exploiting high-performance computing (HPC) resources. The FALL3D+PDAF system can be run in parallel and supports online-coupled DA, which allows an efficient information transfer through parallel communication. Satellite-retrieved data from recent volcanic eruptions were considered as input observations for the assimilation system.

How to cite: Mingari, L., Prata, A., and Pardini, F.: Ensemble-based data assimilation of volcanic aerosols using FALL3D+PDAF, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6774, https://doi.org/10.5194/egusphere-egu21-6774, 2021.

Campi Flegrei is an active volcano located in one of the most densely inhabited areas in Europe and under high-traffic air routes. There, the Vesuvius Observatory’s surveillance system, which continuously monitors volcanic seismicity, soil deformations and gas emissions, highlights some variations in the state of the volcanic activity. It is well known that fragmented magma injected into the atmosphere during an explosive volcanic eruption poses a threat to human lives and air-traffic. For this reason, powerful tools and computational resources to generate extensive and high-resolution hazard maps taking into account a wide spectrum of events, including those of low probability but high impact, are important to provide decision makers with quality information to develop short- and long- term emergency plans. To this end, in the framework of the Center of Excellence for Exascale in Solid Earth (ChEESE), we show the potential of HPC in Probabilistic Volcanic Hazard Assessment. On the one hand, using the ChEESE's flagship Fall3D numerical code and taking advance of the PRACE-awarded resources at CEA/TGCC-HPC facility in France, we perform thousands of simulations of tephra deposition and airborne ash concentration at different flight levels exploring the natural variability and uncertainty on the eruptive conditions on a 3D-grid covering a 2 km-resolution 2000 km x 2000 km computational domain. On the other hand, we create short- and long-term workflows, by updating current Bayesian-Event-Tree-Analysis-based prototype tools, to make them capable of analyze the large amount of information generated by the Fall3D simulations that finally gives rise to the hazard maps for Campi Flegrei.

How to cite: Martínez Montesinos, B., Titos, M., Sandri, L., Barsotti, S., Macedonio, G., and Costa, A.: Probabilistic Tephra Hazard Assessment of Campi Flegrei, Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7595, https://doi.org/10.5194/egusphere-egu21-7595, 2021.

Most of the new supercomputers now use acceleration technology such as GPUs. They promise much higher performance than traditional CPU-only servers, both in terms of floating point operation throughput and memory bandwidth. Furthermore, the electric consumption is significantly reduced, resulting in lower carbon emissions.
However, such high computation speeds can only be achieved if a set of more or less stringent rules are followed with respect to memory access and program flow. As a consequence some algorithms more easily approach peak performance.

Here, we present the results of an effort to achieve high performance on recent nvidia GPU accelerators for the spherical harmonic transform. The spherical harmonic transform can be split into a Legendre transform (which is compute bound) and a Fourier transform (which is memory bound).
By taking advantage of recent algorithmic improvements as well as by tuning the Fourier transform, the can now compute a full forward or backward spherical harmonic transform up to degree 8191 on a single 16GB Volta GPU in less than 0.35 seconds.
For lower resolution (up to degree 1023), a single Volta GPU performs a full transform more than 3 times faster than a 48-cores dual socket Skylake Xeon Platinum server.

We also present results of an ongoing effort to port the (simulation of planetary core fluid and magnetic field dynamics) to GPU-accelerated computers.

How to cite: Schaeffer, N.: Efficient spherical harmonic transforms on GPU and its use in planetary core dynamics simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13680, https://doi.org/10.5194/egusphere-egu21-13680, 2021.

Modern digital seismic networks record a wealth of high-quality continuous waveforms that contain a variety of signals associated to a wide range of seismic sources (e.g., earthquakes, volcanic, tectonic tremors, environmental sources) that probe transient energy release processes. Efficient and automatic detection, location and characterization of these different seismic sources is critical to understand slowly-driven evolution of active tectonic and volcanic systems toward catastrophic events. Developing a common analysis framework for systematic exploration of the increasing wealth of seismic observation streams is important for improving seismic monitoring systems and extracting large and accurately resolved seismic source catalogues.

To this end, we present a scalable parallelization with PyCOMPSs (Tejedor et al., 2017) of the python-based BackTrackBB data-streaming workflow (Poiata et al., 2016; 2018) for automatic detection and location of seismic sources from continuous waveform streams recorded by large seismic networks. This allows achieving an efficient distribution and orchestration of BackTrackBB code on different architectures. PyCOMPSs is a task-based programming model for python applications that relies in a powerful runtime able to extract dynamically the parallelism among tasks and executing them in distributed environments (e.g. HPC Clusters, Cloud infrastructures, etc.) transparently to the users.

We will provide details of the PyCOMPSs-based BackTrackBB workflow implementation. Results of scalability tests and memory usage analysis will be also discussed. Tests have been performed, in the context of the European Centre Of Excellence (CoE) ChEESE for Exascale computing in solid earth sciences, on the MareNostrum4 High-Performance computer of the Barcelona Supercomputing Centre, using large-scale datasets of synthetic and real-case seismological continuous waveform data sets.

How to cite: Poiata, N., Conejero, J., Badia, R. M., and Vilotte, J.-P.: Data-streaming workflow for seismic source location with PyCOMPSs parallel computational framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15141, https://doi.org/10.5194/egusphere-egu21-15141, 2021.

In the last years, the interest in three-dimensional  physico-mathematical models for volcanic plumes has grown, motivated by the need of predicting accurately the dispersal patterns of volcanic ash in the atmosphere (to mitigate the risks for civil aviation and for the nearby inhabited regions) and pushed by improved remote sensing techniques and measurements. However, limitations due to the mesh resolution and numerical accuracy as well as the complexity entailed model formulations, have so far prevented a detailed study of turbulence in volcanic plumes at high resolution. Eruptive columns are indeed multiphase gas-particle turbulent flows, in which the largest (integral) scale is in the order of tens or hundreds of kilometers and the smallest scale is of the order of microns. Performing accurate numerical simulations of such phenomena remains therefore a challenging task.

Modern HPC resources and recent model developments enable the study of multiphase turbulent structures of volcanic plumes with an unprecedented level of detail. However, a number of issues of the present model implementation need to be addressed in order to efficiently use the computational resources of modern supercomputing machines. Here we present an overview of an optimization strategy that allows us to perform large parallel simulations of volcanic plumes using ASHEE, a numerical solver based on OpenFOAM and one of the target flagship codes of the project ChEESE (Centre of Excellence for Exascale in Solid Earth). Such optimizations include: mixed precision floating point operations to increase computational speed and reduce memory usage, optimal domain decomposition for better communication load balancing and asynchronous I/O to hide I/O costs. Scaling analysis and volcanic plume simulations are presented to demonstrate the improvement in both computational performances and computing capability.

How to cite: Brogi, F., Amati, G., Boga, G., Castellano, M., Gracia, J., Esposti Ongaro, T., and Cerminara, M.: Optimization strategies for efficient high-resolution volcanic plume simulations with OpenFOAM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15310, https://doi.org/10.5194/egusphere-egu21-15310, 2021.

The Tjörnes Fracture Zone (TFZ) in North Iceland is the largest and most complex zone of transform faulting in Iceland, formed due to a ridge-jump between two spreading centers of the Mid-Atlantic Ridge, the Northern Volcanic Zone and Kolbeinsey Ridge in North Iceland. Strong earthquakes (Ms>6) have repeatedly occurred in the TFZ and affected the North Icelandic population. In particular the large historical earthquakes of 1755 (Ms 7.0) and 1872 (doublet, Ms 6.5), have been associated with the Húsavı́k-Flatey Fault (HFF), which is the largest linear strike-slip transform fault in the TFZ, and in Iceland. We simulate fault rupture on the HFF and the corresponding near-fault ground motion for several potential earthquake scenarios, including scenario events that replicate the large 1755 and 1872 events. Such simulations are relevant for the town of Húsavı́k in particular, as it is located on top of the HFF and is therefore subject to the highest seismic hazard in the country. Due to the mostly offshore location of the HFF, its precise geometry has only recently been studied in more detail. We compile updated seismological and geophysical information in the area, such as a recently derived three-dimensional velocity model for P and S waves. Seismicity relocations using this velocity model, together with bathymetric and geodetic data, provide detailed information to constrain the fault geometry. In addition, we use this 3D velocity model to simulate seismic wave propagation. For this purpose, we generate a variety of kinematic earthquake-rupture scenarios, and apply a 3D finite-difference method (SORD) to propagate the radiated seismic waves through Earth structure. Slip distributions for the different scenarios are computed using a von Karman autocorrelation function whose parameters are calibrated with slip distributions available for a few recent Icelandic earthquakes. Simulated scenarios provide synthetic ground motion and time histories and estimates of peak ground motion parameters (PGA and PGV) at low frequencies (<2 Hz) for Húsavík and other main towns in North Iceland along with maps of ground shaking for the entire region [130 km x 110 km]. Ground motion estimates are compared with those provided by empirical ground motion models calibrated to Icelandic earthquakes and dynamic fault-rupture simulations for the HFF. Directivity effects towards or away from the coastal areas are analyzed to estimate the expected range of shaking. Thick sedimentary deposits (up to ∼4 km thick) located offshore on top of the HFF (reported by seismic, gravity anomaly and tomographic studies) may affect the effective depth of the fault's top boundary and the surface rupture potential. The results of this study showcase the extent of expected ground motions from significant and likely earthquake scenarios on the HFF. Finite fault earthquake simulations complement the currently available information on seismic hazard for North Iceland, and are a first step towards a systematic and large-scale earthquake scenario database on the HFF, and for the entire fault system of the TFZ, that will enable comprehensive and physics-based hazard assessment in the region.

How to cite: Abril, C., Mai, M., Halldórsson, B., Li, B., Gabriel, A., and Ulrich, T.: Ground motion simulations for finite-fault earthquake scenarios on the Húsavík-Flatey Fault, North Iceland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15557, https://doi.org/10.5194/egusphere-egu21-15557, 2021.