MASTERS THESIS

Application of Hidden Markov Model. Cleveland State University. Under the supervision of Dr. Luiz Felipe Martins.

In my master's thesis, I offer a detailed exploration of Hidden Markov Models (HMMs) in meteorology, focusing on precipitation analysis. I begin by outlining the theoretical foundations of HMMs, tracing their origins to the work of Andrei Markov, whose pioneering research on stochastic processes laid the groundwork for these models. I then explain the mathematical principles that make HMMs suitable for modeling complex weather systems, followed by a comprehensive literature review that examines advancements and challenges in applying HMMs to meteorological data, especially in identifying hidden weather states and transitions crucial for understanding and forecasting precipitation.

The core of my thesis is dedicated to the practical implementation of HMMs for analyzing and predicting precipitation patterns. I include detailed case studies demonstrating how HMMs, grounded in Markov's theories, can be effectively applied in meteorology. To further assist readers, I provide practical Python code examples throughout the thesis, guiding them through the entire modeling process—from data preprocessing and parameter estimation to model evaluation and interpretation of results. These examples are designed to offer practical insights, ensuring that readers feel confident in applying HMMs to their weather prediction models.

I conclude my thesis by discussing the implications of using HMMs to enhance the accuracy of weather forecasts and climate studies. I explore the potential integration of HMMs into existing meteorological frameworks, highlighting their benefits for short-term weather prediction and long-term climate analysis. My thesis aims to be a valuable resource for researchers, meteorologists, and data scientists interested in leveraging HMMs, built on the foundational work of Andrei Markov, for improved precipitation forecasting and climate studies.