Data statistical analysis is a cornerstone of research and has practical applications in atmospheric and climate sciences, serving as the analysis bridge between complex environmental problems and meaningful scientific insights. The atmosphere and climate system are inherently variable, influenced by numerous coupled processes in Earth system that operate across a wide range of spatial and temporal scales, from hourly, daily, to weekly fluctuations in local weather to long-term global climate change spanning decades and centuries. To understand these processes, interpret observational data, and evaluate model outputs, researchers rely heavily on statistical techniques. These methods allow meteorological and climate scientists to identify unique patterns, quantify uncertainties, test hypotheses, and ultimately translate raw data into knowledge that can guide decision-making in encountering extreme events, climate adaptation, disaster risk reduction, and environmental policy.
In this course, students will be introduced step by step to both the theoretical foundations and the practical applications of statistical and machine learning methods widely used in atmospheric sciences. Beginning with fundamental concepts such as sampling procedure, probability distributions, hypothesis testing, and regression analysis, students will build a solid grounding in statistical theory and its applications. From there, the course will advance to more specialized methods commonly applied in weather and climate research, including time series analysis and spatial statistics. These techniques are indispensable for examining long-term climate trends, identifying climate phenomenon such as the El Niño–Southern Oscillation, and evaluating the spatial variability of precipitation or temperature across regions.
In addition, modern developments in machine learning will be covered, providing students with exposure to emerging tools such as clustering methods, logistic regression, and neural networks, which are increasingly being used to improve weather forecasting, climate projection, detect extreme events, and downscale climate model simulations into high-resolution products. Emphasis will be placed not only on the technical details of these methods but also on their interpretation, explainability, limitations, and relevance to real-world atmospheric datasets. By combining lectures with hands-on exercises, the course ensures that students gain practical experience in applying statistical and machine learning tools to raw data, enabling them to critically analyze results and draw scientifically robust conclusions.
By the end of the course, students will have developed the analytical skills necessary to independently conduct statistical analysis in atmospheric and climate sciences. More importantly, they will acquire a deeper appreciation of how statistical reasoning and modern data-driven approaches contribute to advancing our understanding of the Earth’s atmosphere and climate, predicting its future changes, and supporting informed decisions in a world increasingly affected by climate variability and change.
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