Accurate detection of large-scale atmospheric tropical waves, such as African easterly waves (AEWs), may help extend lead times for predicting tropical cyclone (TC) genesis. Sinc observed AEWs have comparable but slightly differnent periods showing spatial and temporal variations, local analysis of AEW periodicity is crucial for predicting the role of AEWs in the modulation of TC genesis. In this work, I investigated the Recurrence plots (RPs) and Recurrence Quantification Analysis (RQA) methods and applied them in order to perform local analyses. A recurrence is defined when the trajectory of a state returns to the neighborhood of a previously visited state. The “recurrence rate” and “determinism” of a RP can be computed to reveal the degree of “predictability” for recurrent solutions. To verify the implementation of these methods using Python, I analyzed various idealized solutions (e.g., periodic, quasi-periodic, chaotic, and limit cycle solutions) using the three-dimensional and five-dimensional Lorenz models (3DLM and 5DLM) and their non-dissipative versions (3D-NLM, 5D-NLM). To reveal the recurrence of multiple AEWs in late August 2006, the methods were then applied in order to analyze the latest European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis interim dataset (referred to here as the ERA-Interim dataset) and the high-resolution simulations from the NASA global mesoscale model.