Cyclone Tracking using TRMM Satellite Data

This dissertation presents an in-depth exploration of global precipitation patterns and cyclone tracking, utilizing data from the Tropical Rainfall Measuring Mission (TRMM) Satellite. This comprehensive study is structured to deepen the understanding of tropical cyclones and their climatic implications.

• Utilized TRMM satellite data to perform a comprehensive analysis of global rainfall patterns and cyclone behaviour, emphasizing the project's use of advanced satellite data for climatological research.

• Employed statistical analysis and data visualization techniques to identify monthly rainfall patterns, wet and dry seasons, and the influence of the Intertropical Convergence Zone (ITCZ) on regional precipitation, showcasing the application of statistical tools in climate data analysis.

• Developed a categorization system for wet days based on rainfall thresholds and conducted a detailed analysis of rainfall frequency and intensity across various cities worldwide, demonstrating proficiency in data classification and quantitative analysis.

• Implemented a first-order Markov chain model to analyse and simulate rainfall patterns in Lagos, Nigeria, illustrating the application of stochastic modelling techniques in understanding precipitation dynamics.

• Developed and tested an innovative algorithm for cyclone tracking using a combination of rainfall and sea level pressure data from TRMM, highlighting skills in algorithm development and the integration of multiple data sources for environmental modelling.

• Evaluated the performance of the cyclone tracking algorithm by comparing its outputs with actual cyclone tracks observed, using precision analysis to validate the algorithm's effectiveness and accuracy.

• Discussed future opportunities and improvements for the methodologies used in rainfall pattern analysis and cyclone tracking, reflecting on the potential for further research and application in disaster management and climate science.

• The algorithm successfully identified multiple major cyclone tracks (four tracks) in the 2019 dataset shown in the figure. 

• This indicates that the region of interest witnessed several cyclonic events during the year.