## 2 Related Work  
WRF's related work on high-performance computers is dedicated to improving the performance, accuracy, and applicability of the model to provide more powerful tools and resources for meteorological research and weather forecasting. Through continuous research and innovation, the performance of WRF models in HPC environments will continue to be improved, bringing more value to scientific research and social applications. The following is the work related to the porting and optimization of WRF on other high-performance computers. [PERSON] [10] used a 1 km-resolution case with over one billion grid points to present results of scaling evaluations on the Cray XT5 for distributed (MPI) and hybrid (MPI/OpenMP) modes of computation. [PERSON] [11] conducted a study on the performance analysis and optimization of nested region simulations using the WRF on the IBM Blue Gene/P platform. This study included exploring various methods to optimize the WRF-based code, resulting in a 29% reduction in the total runtime of the simulation. These optimizations include improving data access patterns, minimizing communication overhead, and optimizing computational workload across available cores, and this study highlights the importance of careful optimization to achieve optimal performance of WRF-based simulations. [PERSON] [12] evaluated the performance and scaling of WRF on three different parallel supercomputers ((a) POWER 775 cluster (p7-IH, IBM POWER7, HFI interconnect), (b) the POWER 460 cluster (Pureflex IBM POWER7, Dual QDR Infiniband interconnect) and (c) the iDataPlex cluster (dx360M4 Intel Sandybridge, FDR14 interconnect)), and its study showed that the performance of WRF's MPI + OpenMP allocation performs differently on different machines, and the optimal configurations of nproc_x and nproc_y in the domain decomposition differ on different machines as well. [PERSON] [5] conducted performance analysis for different components of the WRF model on the Xeon Phi platform to create a set of guidelines to advise WRF users how to efficiently utilize their allocations on Xeon Phi. Their research showed that symmetric execution on Xeon and Xeon Phi can be used for efficient WRF simulations, with significantly faster execution relative to running on any isomorphic architectur. [PERSON]'s [13] review of the available IO methods listed in the widely used Weather Research and Forecasting (WRF) model highlights the new quilt server + parallel NetCDF (PNETCDF_QUILT) technology, which on the Cray XC40 platform using an asynchronous IO (quilt) server and parallel NetCDF. [PERSON] [14] designed benchmarks for operational configurations using the WRF model in the Indian region based the supercomputer of University Grants Commission center. They performed evaluations to find the optimal computational resources required to implement the WRF model for real-time workflows and considered various aspects of the scalability and IO performance of the WRF model on HPC. [PERSON] [8] investigated the impacts of different process distributions on simulation times and the reasons behind them, and suggested a better distribution algorithm than the WRF implementation on the GALGO Supercomputer. He also examined the cost of reducing the wall clock time of the simulation by increasing the number of processing resources used, and determined the loss of computing efficiency in the same work. The above shows that many researchers have optimized WRF on different high performance platforms. Their results show that the performance obtained by the default configuration of WRF after porting to a new HPC server is not the optimal performance,and the optimal performance configurations exhibited by WRF on different platforms varies greatly. Therefore, it is necessary to optimize WRF on its own platform in order to obtain the best performance. Unlike previous studies, this study focuses on the performance evaluation and optimization of WRF on the Kunpeng 920 processor platform.