A Dynamic HPC Probabilistic Tsunami Forecast Workflow for Real-time Hazard Assessment

The Urgent Tsunami Computing procedures discussed herein are designed to quantify potential hazards resulting from seismically-induced tsunamis following an earthquake, with a temporal scope ranging from minutes to a few hours. The presented workflow employs comprehensive simulations, encompassing the entire tsunami propagation process, while accounting for uncertainties associated with source parameters, tsunamigenesis and wave propagation dynamics. Within the EuroHPC eFlows4HPC project, we present a High-Performance Computing (HPC) workflow tailored for urgent tsunami computation in which the Probabilistic Tsunami Forecast (PTF) code has been restructured and adapted for seamless integration into a PyCOMPSs framework. This framework enables parallel execution of tasks and includes simulations from Tsunami-HySEA numerical model within a unified computational environment. Of particular significance is the workflow's capability to incorporate new datasets, such as focal mechanism data, seismic records, or real-time tsunami observations. This functionality facilitates an "on-the-fly" update of the PTF, ensuring that the forecasting model remains responsive to the latest information. The development of this workflow involves a systematic exploration of diverse scenarios, realistic simulations, and the assimilation of incoming data. The overarching goal is to rigorously diminish uncertainties, thereby producing updated probabilistic forecasts without compromising precision and enhancing risk mitigation efforts far from the seismic source. Improved risk management, achieved by informing decision-making in emergency situations, underscores the importance of this development. We will showcase the technical advancements undertaken to tailor the workflow for HPC environments, spanning from the developers' perspective to that of the end user. Additionally, we will highlight the scientific enhancements implemented to leverage the full potential of HPC capabilities, aiming to significantly reduce result delivery times while concurrently enhancing the accuracy and precision of our forecasts.