Legacy scientific codes are essential to long-standing operational workflows but often cannot fully exploit modern heterogeneous high-performance computing (HPC) architectures. GLOBO, a global atmospheric circulation model developed at CNR-ISAC, is one such model that is still used for research and operational weather forecasting. In this work, we present an ongoing refactoring of GLOBO to improve its scalability and prepare it for GPU acceleration and cloud execution. The modernization focuses on three main areas: dynamic memory allocation, replacement of legacy point-to-point MPI communications with optimized collective operations, and OpenACC-based GPU offloading. We describe the redesign of the communication strategy, which replaces multiple MPI_Isend/MPI_Irecv exchanges with collective primitives (MPI_Bcast, MPI_Scatterv, MPI_Sendrecv, MPI_Reduce) to reduce overhead and improve parallel efficiency. Scalability experiments across multiple spatial resolutions show that a new collective communication strategy consistently matches or exceeds the original implementation, delivering up to 40% runtime reduction in high-resolution, multi-node configurations. These results lay the groundwork for refactoring GLOBO toward a fully dynamic, GPU-accelerated, cloud-ready execution, preserving its scientific heritage while ensuring sustainable performance on future HPC platforms.

Global changes are increasing the frequency and intensity of extreme weather events, posing challenges for forecasting localized phenomena with sub-grid resolution. The Hi-WeFAI project addresses this by combining high-performance computing, Federated Artificial Intelligence, and heterogeneous sensor networks to improve short-term precipitation forecasting and flood nowcasting. In this paper, we present preliminary results using a transformer-based radar prediction model coupled with a flood model to generate high-resolution early warning maps. The results from the Naples pilot site improved accuracy and detail, highlighting the potential of the hybrid AI and HPC approach to support quasi-real-time decision-making and disaster risk reduction.

This paper introduces DAGonCAPIO, a workflow framework that integrates the DAGonStar engine with the CAPIO middleware to enable I/O streaming in scientific workflows. DAGonStar utilizes a Directed Acyclic Graph (DAG) to orchestrate tasks. At the same time, CAPIO enables downstream tasks to process data as soon as partial outputs become available, without requiring modifications to application code. This integration reduces delays from traditional file-based communication. The system uses the workflow:// schema to define data dependencies and generate CAPIO coordination scripts. DAGonStar was modified to support early scratch directory naming and decoupled task execution. Experiments with a WRF-based weather forecasting workflow on a 256-core cluster demonstrate that DAGonCAPIO reduces time-to-first-result by up to 4171 seconds, achieving a nearly 10x speedup.