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The need to understand the role of statistical methods for the forecasting ofclimatological parameters cannot be trivialized. This study gives an in depthreview on the different variations of the Mann-Kendall (M-K) trend test and howthey can be applied, regression techniques (Simple and Multiple), theAngstrom-Prescott model for solar radiation, etc. The study then goes ahead toapply some of them with data obtained from the Nigerian Meteorological Agency(NiMet), and applying tools like the python programming language and WolframMathematica. Results show that the maximum ambient temperature for Calabar isincreasing (Z=2.52) significantly after the calculated p-value < 0.05(significant level). The seasonal M-K test was also applied for the dry and wetseasons and both were found to be increasing (Z=3.23 and Z=4.04 respectively)after their calculated p-values < 0.05. The relationship between refractivityand other meteorological parameters relating to it was discerned using partialdifferential equations giving the gradient of each with refractivity; this wascompared with results from the correlation matrix to show that the water vapourcontents of the atmosphere contributes significantly to the variation ofrefractivity. Multiple linear regression has also been adopted to give anaccurate model for the prediction of refractivity in the region after theresidual error between the calculated refractivity and predicted refractivitywas minimal.

Forecasting of Meteorological variables using statistical methods and tools