Speaker
Description
Coronal mass ejections (CMEs) are key drivers of space weather disturbances. Therefore, it is necessary to have accurate prediction models for both their scientific understanding and operational forecasting. The Probabilistic Drag-Based Model (P-DBM) is a Monte Carlo-based framework to simulate the heliospheric propagation of CMEs, yielding probabilistic predictions of CME arrival times, impact velocities, and the associated uncertainties.
Although P-DBM demonstrates robust predictive capabilities, its operational flexibility has been constrained by observational input limitations. To address these constraints, we have now implemented a comprehensive data-driven approach that boosts the model’s predictive capabilities significantly. Our data-driven framework includes the development of two critical data products: (1) a comprehensive catalogue of geo-effective CME/ICME characteristics and (2) a systematically constructed CME-ICME lineup catalogue that enables a much improved model parameter correlation.
Additionally, we have developed a sophisticated web-based application to maximise accessibility and foster collaboration within the space weather community. This web application enables real-time model execution, visualisation tools and export capabilities for both research and operational applications.
