A study of the high-performance computing parallelism in solving complexity of meteorology data and calculations

This paper investigates the high-performance computing (HPC) implementation in the field of meteorology, the challenges, and the future benefits. It is associated with utilizing HPC parallelism to simultaneously execute multiple tasks or operations for meteorological research. Merely a few people are aware of HPC's role in generating weather forecasting, climate modeling, and data assimilation. Our investigation elaborates on, identifies, and analyzes the features and characteristics of parallel computing that are utilized in it. The paper also focuses on examining parallelization modeling, the algorithms involved, and optimization strategies employed in HPC-enabled meteorological simulations. By addressing significant aspects of HPC in meteorological research, it helps the scientific community identify emerging trends and future directions for leveraging HPC in meteorology. Further issues can be studied for integrating big data analytics and machine learning into HPC computing architectures.

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