Meteorology and climate research rely on both station observation and reanalysis techniques. Station observation provides real-world data, while reanalysis techniques provide consistent weather information on a global scale and over continuous time by integrating multiple observational data and numerical models (Edwards, 2010; Rummukainen, 2012; Hersbach et al., 2020). In scientific research and application, it is often necessary to combine both to obtain more comprehensive meteorological data (Salcedo-Sanz et al., 2020; Hu et al., 2019; Schauberger et al., 2020). Simulation models rely on physical theory. Numerical models were developed by weather forecasters to compute large-scale atmospheric movements and anticipate weather patterns (Parker 2016). Subsequently, climate scientists adopted similar methodologies to simulate the Earth's climate over extended periods, ranging from years to decades (Pitman 2003; Jiao et al 2021). Additionally, by modifying the simulated variables and conditions, they utilized models to forecast how climate patterns will evolve as human activity affects the composition of the atmosphere and other climate-related systems.