Syllabus

Introduction to weather and climate variables. Statistical and dynamical analysis of earth observations and model outputs. Overview of the global, regional and mesoscale models. Visualization of weather and climate variables. Various softwares (ncview, netcdf, GrADS, CDO, NCO) and libraries including python programming and bash scripting.

This course aims to provide a comprehensive understanding of weather and climate variables, along with statistical and dynamical techniques for analyzing observation data from various sources and model outputs. Students will gain insights into global and models and develop skills in visualizing climate data. The course also introduces various software tools such as Ncview, NetCDF, GrADS, CDO, and NCO, along with programming techniques using Python and Bash scripting for efficient data processing and analysis.

To introduce students about various forms of datasets (.csv, .nc, .dat etc) and to open them using several software.
To install Anaconda platform to perform tasks on Jupyter notebook using basic python programming.
Apply several python libraries, such as netcdf4, xarrays, numpy, pandas, matplotlib etc for data analysis and plotting.
Apply learned python skills to create panel plots for monthly, seasonal, annual variability in climate variables as well as detect climate change.

Hartmann D. L, Global Physical Climatology, Academic Press Inc., , 1994

source:, https://www.w3schools.com/python/,

source, https://matplotlib.org/,