As the computing power of large-scale HPC clusters approaches the Exascale, the gap between compute capabilities and storage systems is ever widening. In particular, the popular High Performance Computing (HPC) application, the Weather Research and Forecasting Model (WRF) is being currently being utilized for high resolution forecasting and research which generate very large datasets, especially when investigating transient weather phenomena. However, the I/O options currently available in WRF have been found to be a bottleneck at scale. In this work, we demonstrate the impact of integrating a next-generation parallel I/O framework - ADIOS2, as a new I/O backend option in WRF. First, we detail the implementation considerations, setbacks, and solutions that were encountered during the integration. Next we examine the results of I/O write times and compare them with results of currently available WRF I/O options. The resulting I/O times show over an order of magnitude speedup when using ADIOS2 compared to classic MPI-I/O based solutions. Additionally, the node-local burst buffer write capabilities as well as in-line lossless compression capabilities of ADIOS2 are showcased, further boosting performance. Finally, usage of the novel ADIOS2 in-situ analysis capabilities for weather forecasting is demonstrated using a WRF forecasting pipeline, showing a seamless end-to-end processing pipeline that occurs concurrently with the execution of the WRF model, leading to a dramatic improvement in total time to solution.