Understanding the relationship between the variations of meteorological parameters is vital in tackling the climatic problem. This paper presents methods for analyzing parameters that relate directly and indirectly to each other and accurate methods for interpreting their results. Using obtained data for 14 years and calculated data for other parameters, we adopt the Mann-Kendall (M-K) test for the trend analysis of the annual and seasonal variations, the correlation matrixes, and linear regression pair plots to discern the relationship between all parameters using the python programming software. To crystalize results, partial derivatives relating the equivalent potential temperature (EPT) for a pseudo-adiabatic process with parameters affecting its variation from equations are being obtained. The magnitude of these derivatives' gradients was used to bolster regression results, showing the mixing ratio (MR) of air as the parameter with the most effect on EPT variation. The MK test results show that the atmospheric pressure (AP) and average ambient temperature (AT) were all increasing significantly for all variations (annual, dry and wet seasons). In contrast, others varied between dry and wet seasons after adopting a benchmark significance level of 5% (0.05). The correlation matrixes and linear regression pair plots show a strong relationship between the variations of refractivity, EPT, the temperature at the lifting condensation level (TL), MR, vapor pressure (VP), specific humidity (SH), and the dew point temperature (DPT). The potential temperature (PT), saturated vapor pressure (SVP), saturated mixing ratio (SMR), and the AT relationships showed a robust positive correlation/regression. This correlation offers a connection between the AT and the PT. The processes, including the partial derivatives, pair plots, correlation matrixes, and tests for trends, provide a solution to the meteorological analysis problem. Results and methods can be applied in other regions.