Smoothing Plots With Python, Matplotlib and ERA Temperature Data (for UK Regions)

Building on the same techniques as discussed in my last blog, which focused on using python and matplotlib to plot ERA5 temperature data in West Africa. These plots illustrate the effect of gaussian filter smoothing on hourly ERA5 temperature data, obtained from the Climate Data Store. Gaussian filters are linear and typically used to reduce noise.

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Using ERA5 data to plot temperature in West Africa

A quick/dirty tech blog today, getting to know some of Matplotlib’s extra features for generating some attractive plots!

Using yearly ERA5 temperature data from 1979 to present obtained from the Copernicus Climate Data Store, the data was masked by country using shapefiles from Natural Earth and then an average was taken for the area (see previous blogs on area averaging for information on how to do this).

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Metadata Generation for CSV Files

For any data produced with the intention of being downloaded and used by other users, it important to include information on the dataset. For example, details on the data origins should be provided, such as who produced the dataset, who can be contacted about the data and when/where it was produced. In addition, properties on the dataset itself, such as variable names and units of measurement all help the end user in comprehending the data.

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Calculating NUTS2 Regional Averages with Land Sea Mask

This post serves as a continuation on the techniques described in my last blog post. So please familiarise with those steps beforehand.


As before, load in the NUTS shapefiles, this time selecting NUTS2.

NUTS2 is higher resolution and as a result, there are many more shapefiles. NUTS2 contains polygons at a regional (sub-country) level. In total there are 332 shapes for the Eurostat EU region.

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ERA5 10m/100m Wind Speed Analysis

I wanted to see if I could improve on the standard power law coefficient (1.389495) for calculating 100m wind speed from 10m data. The currently available ten years of ERA5 U and V netCDF wind components from CDS were concatenated, then calculated for wind speed using CDO; a handy collection of command-line operators to manipulate and analyse climate and numerical weather prediction data:

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Examining NetCDF Files in R

I am currently working for a non profit organisation, working on enhancing the interaction between the energy industry and the weather, climate and broader environmental sciences community.

Not coming from a climate science background, I had to become quickly accustomed to the terminology and technologies associated with this field of research. A commonly used file types produced to represent weather and climate data, are Network Common Data Files (aka NetCDF files).

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