Data Visualization

Data visualization plays a crucial role in atmospheric science, allowing researchers to analyze and interpret complex datasets effectively. In this section, we’ll explore various techniques for visualizing atmospheric data using Jupyter notebooks.

Maps

This tutorial demonstrates how to visualise data from the Copernicus Atmosphere Monitoring Service (CAMS) in the form of two dimensional maps.

Animations

This tutorial demonstrates how to create animations from data of the Copernicus Atmosphere Monitoring Service (CAMS). We will use as our example Organic Matter Aerosol Optical Depth (AOD) analysis data from the beginning of August 2021 over North America. This was a time of significant wildfire activity.

Time Series

This tutorial demonstrates how to plot time series of data from the Copernicus Atmosphere Monitoring Service (CAMS). The example focusses on CO2, and we will visualise the “Keeling Curve” of global average increases in CO2 from the last decades.

Profile Plots and Zonal Means

This tutorial demonstrates how to visualise Copernicus Atmosphere Monitoring Service (CAMS) data of different atmospheric levels in the form of profile plots and zonal mean plots. The data used in this tutorial includes CO from the Northern Hemisphere during the summer (peak fire season) from 2003 to 2021.