The PASC Conference is an interdisciplinary forum for scientific computing and high-performance computing, co-sponsored by ACM and CSCS. PASC26 was held from June 29 to July 1, 2026 at the University of Bern, Switzerland, under the theme "Building Trust in Science through HPC Co-Design."
ClusterTech presented cutting-edge atmospheric modeling research at the conference. Mr. Sai Lun Tin, Computational Scientist at ClusterTech, showcased a technical poster titled: "Predictive Alerts and Atmospheric Data for Airport Windshear by CPAS 200m Weather Model" – highlighting the operational forecasting capabilities of the ClusterTech Platform for Atmospheric Simulation (CPAS). This poster demonstrated how CPAS's 200-meter resolution simulation enables day-ahead prediction of low-level wind shear (LLWS) at Hong Kong International Airport (HKIA), significantly advancing weather risk assessment for aviation planning within the critical nowcasting-to-forecasting transition.
Key innovations included:
- Use of Adaptive Mesh Refinement (AMR) for computational efficiency, triggering the 200-m high-resolution simulation with Lantau mesh on a conventional CPU-based HPC system.
- Elimination of abrupt changes in lateral boundary conditions through global-to-local unstructured grid architecture.
- Hierarchical Time-Stepping for substantial reduction in computational requirements across varying resolution scales.
This research directly addresses core operational challenges where current mitigation relies on real-time detection and short-term forecasting, while day-ahead prediction remains essential for flight safety and cost optimization management. The presentation attracted strong interest from global atmospheric and HPC experts at the premier interdisciplinary forum, validating CPAS's approach to high-fidelity atmospheric simulation for mission-critical applications.
ClusterTech continues to advance CPAS capabilities for aviation meteorology, urban weather prediction, extreme event forecasting, and renewable energy applications such as wind and solar power generation forecasting.
Read more about PASC 2026: https://pasc-conference.org/editions/pasc26/presentation/?id=pos123&sess=sess129