Climate Tools and Tricks

Published in University of Oxford, 2020

A collection of packages, code snippets and gists to import, modify, and analyse Climate Data.

Importing Climate Data


CliMetLab is a Python package aiming at simplifying access to climate and meteorological datasets, allowing users to focus on science instead of technical issues such as data access and data formats. It is mostly intended to be used in Jupyter notebooks, and be interoperable with all popular data analytic packages, such as NumPy, Pandas, Xarray, SciPy, Matplotlib, etc. and well as machine learning frameworks, such as TensorFlow, Keras or PyTorch. See Overview for more information.

Documentation here.

Hockeystick Package

The goal of hockeystick is to make essential Climate Change datasets easily available to non-climate experts. hockeystick users can download the latest raw data from authoritative sources as well as view it via pre-defined ggplot2 charts. Datasets include atmospheric CO2, instrumental and ice-core temperature records, sea levels, and Arctic/Antarctic sea-ice. Additional visualizations using this data will be added over time.

Vignette Github Link


Import Cropland Data into R using CropScapeR by Bowen Chen Github Link

Weather Forecasting

An introduction to weather forecasting with deep learning by RStudio:

Global weather is a chaotic system, but of much higher complexity than many tasks commonly addressed with machine and/or deep learning. In this post, Sigrid Keydana from the RStudio team provides a practical introduction featuring a simple deep learning baseline for atmospheric forecasting. While far away from being competitive, it serves to illustrate how more sophisticated and compute-intensive models may approach that formidable task by means of methods situated on the “black-box end” of the continuum.

GIS and creating Maps

Satellite Imagery

How to create a satellite image time lapse