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As the computing power of large-scale HPC clusters approaches the Exascale,the gap between compute capabilities and storage systems is ever widening. Inparticular, the popular High Performance Computing (HPC) application, theWeather Research and Forecasting Model (WRF) is being currently being utilizedfor high resolution forecasting and research which generate very largedatasets, especially when investigating transient weather phenomena. However,the I/O options currently available in WRF have been found to be a bottleneckat scale.  In this work, we demonstrate the impact of integrating a next-generationparallel I/O framework - ADIOS2, as a new I/O backend option in WRF. First, wedetail the implementation considerations, setbacks, and solutions that wereencountered during the integration. Next we examine the results of I/O writetimes and compare them with results of currently available WRF I/O options. Theresulting I/O times show over an order of magnitude speedup when using ADIOS2compared to classic MPI-I/O based solutions. Additionally, the node-local burstbuffer write capabilities as well as in-line lossless compression capabilitiesof ADIOS2 are showcased, further boosting performance. Finally, usage of thenovel ADIOS2 in-situ analysis capabilities for weather forecasting isdemonstrated using a WRF forecasting pipeline, showing a seamless end-to-endprocessing pipeline that occurs concurrently with the execution of the WRFmodel, leading to a dramatic improvement in total time to solution.

High Performance Parallel I/O and In-Situ Analysis in the WRF Model with ADIOS2