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With the approach of Exascale computing power for large-scale HighPerformance Computing (HPC) clusters, the gap between compute capabilities andstorage systems is growing larger. This is particularly problematic for theWeather Research and Forecasting Model (WRF), a widely-used HPC application forhigh-resolution forecasting and research that produces sizable datasets,especially when analyzing transient weather phenomena. Despite this issue, theI/O modules within WRF have not been updated in the past ten years, resultingin subpar parallel I/O performance.  This research paper demonstrates the positive impact of integrating ADIOS2, anext-generation parallel I/O framework, as a new I/O backend option in WRF. Itgoes into detail about the challenges encountered during the integrationprocess and how they were addressed. The resulting I/O times show an overtenfold improvement when using ADIOS2 compared to traditional MPI-I/O basedsolutions. Furthermore, the study highlights the new features available to WRFusers worldwide, such as the Sustainable Staging Transport (SST) enablingUnified Communication X (UCX) DataTransport, the node-local burst buffer writecapabilities and in-line lossless compression capabilities of ADIOS2.  Additionally, the research shows how ADIOS2's in-situ analysis capabilitiescan be smoothly integrated with a simple WRF forecasting pipeline, resulting ina significant improvement in overall time to solution. This study serves as areminder to legacy HPC applications that incorporating modern libraries andtools can lead to considerable performance enhancements with minimal changes tothe core application.

accelerating wrf i/o performance with adios2 and network-based streaming