EarthKit simplifies workflows in weather and climate science by streamlining data access, processing, analysis, and visualization. Designed to eliminate technical complexities, it enables efficient data handling without concerns about formats or file management.

As an open-source Python project led by the European Centre for Medium-Range Weather Forecasts (ECMWF), EarthKit now integrates the WEkEO Harmonized Data Access (HDA) into the earthkit package. This integration allows seamless retrieval of most WEkEO datasets through EarthKit’s wrapper functions, further enhancing accessibility and efficiency in meteorological and climate research.

This artcile gives an overview on how to use the Earthkit WEkEO Plugin for data access, analysis and visualization.

Before accessing and processing data, EarthKit must be installed and configured. This section covers the installation process and how to set up caching to optimize dataset retrieval and reuse.

The WEkEO plugins are included in the earthkit package, so you will only need to download the earthkit package for running this notebook:

Next, import the necessary packages and the freshly installed earthkit package:

EarthKit's cache feature detects repeated dataset requests and reuses previously downloaded data. A temporary cache or a custom cache directory can be configured, as shown below with the "cache" folder.

EarthKit simplifies data retrieval and management by providing seamless access to WEkEO datasets. This section covers how to download datasets, leverage caching for efficiency, and convert data into a structured format for analysis.

The earthkit.data.from_source function loads datasets from the WEkEO HDA. When first downloading, configure your WEkEO username and password in the pop-up below the code cell.

The first argument is always "wekeo-source", and the second is the request for the WEkEO HDA. You can reuse past requests or retrieve the API request from the WEkEO Viewer.

The data is cached, so no file management is needed. It will be deleted when the cache is cleared. Re-running ekd.from_source with the same dataset and parameters uses the cached data instead of downloading again, making execution faster.

More information on the caching of Earthkit can be found in the official documentation: Earthkit Caching Documentation.

The earthkit.to_xarray function converts the downloaded data into an xArray.

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Once data is retrieved and converted into xArray, it can be explored and processed for analysis. This section covers how to select subsets, perform calculations on time steps, and merge multiple datasets for comparison.

After converting to xArray, you can explore, analyze, and visualize the data using xArray functions.

The example below selects a subset by slicing latitude and longitude to approximate Germany. Then, it averages all 24 hours using mean() and visualizes the result as a line chart with plot.line.

Output:

Arithmetic operations can be performed on different time steps of the dataset. Here, two data arrays from single time steps are subtracted to create a temperature difference map, showing the temperature change between 00:00 UTC and 12:00 UTC globally. This difference reflects the day-night cycle inversion across the globe.

Output:

With the WEkEO Earthkit Plugin, datasets from different sources can be downloaded and combined. This section covers handling multiple datasets by adding daily temperature data from January 1, 2019, for comparison with the previously downloaded monthly temperature means.

Since both datasets use the same variable name (t2m) for temperature, at least one must be renamed before merging:

The merge function combines datasets with identical dimensions. For datasets with different characteristics, consider using concat, combine_by_coords, or combine_nested. Detailed documentation on these functions is available here.

You can now compare the daily temperature on the 1st of January 2019 against the mean temperature in January in Germany.

Output:

Exporting the created data subsets for later use is recommended, especially for large datasets. Saving intermediate results prevents the need to redownload data when the Jupyter Hub cache is cleared, significantly saving time.

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Earthkit provides built-in plotting capabilities to visualize data retrieved from the HDA. This section presents examples of how to plot different types of data using earthkit-plots. The available visualization methods vary depending on the dataset. For a complete overview, refer to the earthkit-plots documentation

To proceed with visualization, two different datasets from the previous sections will be used:

Load the datasets using earthkit.data.from_source:

With the data loaded, the next step is visualization using earthkit-plots.

This example visualizes and compares two datasets for the same region using earthkit-plots:

Side-by-side maps reveal differences between air and land surface temperatures, created with the earthkit-plots Figure class for Europe:

Output:

This example compares datasets across regions using earthkit-plots:

Side-by-side maps highlight regional temperature variations, created with the earthkit-plots Figure class:

Output:

This example demonstrates how to visualize multiple time steps of a dataset using earthkit-plots. Each subplot represents a different timestamp of ERA5 Air Temperature (2m) over Europe, allowing for an easy comparison of temporal variations:

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Regardless of how you choose to contact us, you will first be put in touch with our AI Agent Neo. At any time, you can reach a member of the WEkEO User Support team by clicking on "talk to a person" via chat, or by naturally requesting it in reply to Neo's email.