## 1 Introduction  
The planetary boundary layer height (PBLH) and essential meteorological variables--such as temperature, humidity, horizontal and vertical wind speeds, and wind direction--are vital for understanding atmospheric dynamics and the transfer of heat, moisture, and pollutants between the Earth's surface and the atmosphere [1,2]. The accurate characterization of these variables is essential for reliable weather predictions, air quality modeling, and climate research. Among the widely utilized tools for such studies, the ERA5 reanalysis dataset offers extensive temporal and spatial coverage of these variables, making it an invaluable resource for large-scale meteorological and climate applications. ERA5 (the fifth generation of the ECMWF atmospheric reanalysis of the global climate) is a high-resolution global reanalysis dataset that integrates various observational data and advanced numerical weather prediction models, providing accurate, continuous, and consistent atmospheric and surface meteorological variables [3,4]. It has made significant research progress across multiple fields. [PERSON] et al. (2024) developed an improved weighted mean temperature (Tm) model, named EGWMT, for accurate Tm estimation at any site in Egypt, utilizing hourly ERA5 reanalysis data from 2008 to 2019 [5]. The EGWMT model was evaluated against two data sources, including ERA5 data from 2019 to 2022 and radiosonde profiles from 2017 to 2022, and was shown to outperform existing models (Bevis, Elhaty, ANN, and GGTm-Ts) with significant improvements in RMSE and mean absolute bias (MAB), confirming its superior performance for GNSS-based precipitable water vapor retrieval. [PERSON] et al. (2024) evaluated the applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States, highlighting significant biases in ERA5-derived wind and solar power [6]. They found that ERA5 overestimates solar power and underestimates wind power, with errors varying by season and location. [PERSON] et al. (2024) evaluated the performance of the ERA5 reanalysis data in representing precipitation for the Brazilian side of the Mirim-Sao Goncalo watershed (MSGW). By comparing ERA5 grid point data with observed rain gauge data from 1981 to 2020, their study found that ERA5 effectively captured precipitation patterns in the region, showing low error measures (MAPE and RMSE) and high correlation (rpearson), demonstrating the model's potential for water resource management and climate data analyses [7]. [PERSON] et al. (2024) used ERA5 data to analyze tornadic storm environments in Canada from 1980 to 2020 [8]. They found that ERA5 convective parameters accurately represent observed data, with eastern Canada having more favorable conditions for tornado formation due to higher humidity, while central Canada had the highest CAPE. Despite its high spatial and temporal resolution, ERA5 data may lack sufficient detail in regions with complex terrain, such as mountainous plateaus and coastal areas. This limitation can result in errors and biases when representing the meteorological and climatic features of these regions [9, 10, 11]. A number of studies have investigated the performance and suitability of ERA5 reanalysis data [12,13]. [PERSON] et al. (2024) assessed the accuracy and impact of ERA5 reanalysis precipitation data on hydrological cycling in the IIi River Basin (IRB) and tested several bias correction methods, including linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM) [14]. They found that ERA5 overestimates precipitation, with the DM method providing the most effective bias correction, significantly improving runoff simulations and addressing inaccuracies in peak flow and flow duration curves. [PERSON] et al.