Courses

The course provides a solid foundation in atmospheric numerical modeling, covering governing equations, numerical methods, and data assimilation. Students explores physical parameterizations, predictability, and ensemble forecasting. Hands-on experience is provided with regional and global models. The course emphasizes solving PDEs, running models, and analyzing forecasts using Python and objective analysis.
Taught 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025

This course provides an overview of numerical modeling for weather and climate forecasting, with emphasis on the mathematical and computational foundations. Students explores numerical and analytical solutions of governing equations, types of horizontal and vertical grids, numerical stability and accuracy, and sources of model error. Key concepts include finite difference methods, grid design, time-stepping schemes, and idealized model experiments. The course builds a strong conceptual and practical basis for understanding and evaluating forecast models.
Taught 2017, 2018, 2020, 2022, 2023, 2024, 2025

The course provides essential computational skills for meteorology, with hands-on training in Fortran, Python, Unix/Linux environments, and shell scripting. Students explores practical applications such as data processing, visualization, and numerical model configuration. Emphasis is placed on writing efficient code, automating workflows, and handling large atmospheric datasets. The course prepares students to work with real-world meteorological models and tools.
Taught 2018, 2020, 2021

Fundamentals of classical mechanics: motion, forces, energy, momentum, and rotational dynamics.
Taught 2017, 2018, 2019, 2020, 2021, 2022

Fundamentals of vectors, conic sections, quadric surfaces, and spatial geometry.
Taught 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025

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