Time series analysis is of paramount importance in ocean and climate research fields. It allows scientists to study long-term trends and variations in critical environmental parameters, such as sea surface temperatures, ocean currents, atmospheric conditions, and carbon dioxide levels. This analysis provides insights into the complex and dynamic nature of the Earth's climate system, helping us understand past patterns, predict future changes, and assess the impact of climate change on ecosystems and societies. By deciphering the patterns hidden within these time series data, researchers can make informed decisions, mitigate risks, and develop effective strategies for addressing the profound challenges posed by climate change and its consequences for our planet.
In our earlier post, we explored the techniques for importing, reading variables, and visualizing NetCDF data. If you didn't catch it, you can find the link here http://theaireenproject.com/2023/10/19/the-world-of-netcdf/
For this tutorial, we'll continue to work with the same data featured in the earlier post, focusing on various time series plotting techniques.
The complete code that we will be learning today is available in the Jupyter notebook (Time Series Plotting.ipynb), accessible here: https://github.com/akashspunnayil/ClimoMarineLab/tree/main/Python
1. Load and read variables
2. Select a single location for the following plots
3. Plot a time series of single location - LINE CHART
4. BAR CHART
5. SCATTER PLOT
6. Basic statistics
Mean, Standard Deviation, Minimum, Maximum, Median
Requested tutorial
Could you please create a tutorial on plotting a trend line over a time series?
- Anonymous
Trend Line over the time series
Happy coding!
Cheers!
Subscribe, share and comment.
Stay tuned for more classes.
