Complexity and nonlinear trend in the internal activities of the troposphere has been a great factor affecting the transmission and receiving of good quality of signals globally. In lieu of this, prediction of chaos and positive refractivity gradients for line-of-sight microwave radio paths is necessary for designing radio systems. Complexity in the troposphere due to changes in meteorological parameters can lead to the strong negative gradient (or super-refraction) which afterward lead to interference between terrestrial links and satellite earth stations. In this paper, a comparative study on the degree of complexity of Radio Refractivity Gradient (RRG) using Chaotic Quantifiers (CQ) such as Phase Plot Reconstruction (PPR), Average Mutual Information (AMI), False Nearest Neighbor (FNN), Lyapunov Exponent (LE), Tsallis Entropy (TS) and Recurrence Plot (RP) are discussed extensively. The RRG data (2011-2012) used in this work were obtained for 0 m to 100 m, from the archives of Tropospheric Data Acquisition Network (TRODAN) from five different stations namely; Akure ( Geo . ), Enugu ( Geo . ), Jos ( Geo . ), Minna ( Geo . ) and Sokoto ( Geo . ). The chaotic quantifiers are used to investigate the degree of complexity in the 30 minutes interval atmospheric data from the selected locations which is specified into rainy, dry and transition season months. The parallel and short diagonal lines observed depicts the evidence of chaos. However, the observed result shows that the RRG is higher during the rainy season than the dry season. In other words, the information is valid for the proposed data analysis, since the LE is actually directly proportional to the TE. Also, the results further show that the rainy season months exhibit higher chaoticity than the dry season months, which is equivalent to high radio refractivity gradient observed across the selected stations.

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