Climate Data Science & Modeling

A free, hands-on curriculum: from your first Python notebook to running WRF and MITgcm — with real climate data at every step.

Python & Xarray Statistics & ML GIS & Earth Engine WRF · MITgcm · SPEEDY

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00 ClimBuntu OS

ClimBuntu OS — Linux for climate research

ClimBuntu (Climate + Ubuntu) is a free Linux distribution I built for climate research — numerical climate models (WRF, MITgcm, SPEEDY), geospatial tools (QGIS), and the scientific Python stack, all preinstalled and ready to run. It removes the setup barrier so students and researchers can get straight to the science.

Explore ClimBuntu → Download V1.0 Install guide

01 Python

1.1 Introduction to Python (Basic) Introduction to Python (Basic)
1.2 Numpy, Pandas & Matplotlib Introduction to Python: Numpy, Pandas & Matplotlib

02 Climate Data

2.1 NetCDF datasets NetCDF datasets
2.2 How to plot How to plot
2.3 Downloading Climate Data Downloading Climate Data

03 Analysis and Methods

3.1 Seasonal Analysis with Xarray Seasonal Analysis with Xarray
3.2 Anomalies and timeseries plot How to make anomalies and plot timeseries
3.3 Linear Regression Linear Regression
3.4 Statistical analysis Statistical analysis: Correlation, Composite mean, and Significance testing.

04 GIS And Remote Sensing

4.1 GIS and Remote Sensing GIS and Remote Sensing with Python
4.2 Basics of GIS using Python Basics of GIS using Python
4.3 Flood Mapping with GEE Flood Mapping with Google Earth Engine
4.4 ARCpy ARCpy

Machine Learning Projects

Project 1

Predicting ENSO with Machine Learning

Climate Modeling Tutorial

WRF WRF
MITgcm MITgcm
SPEEDY speedy
Contributors 10