Add preprocessor to extract surface values from 3D atmospheric variables

[jlenh]

Description

This pull request introduces a new preprocessor function to extract the surface values from 3D atmospheric variables and corresponding surface pressure ps. The preprocessor function is largely based on the implementation done for the derivation of surface CO2 co2s at https://github.com/ESMValGroup/ESMValCore/blob/main/esmvalcore/preprocessor/_derive/co2s.py. It thus avoids having to create either at standalone _derive preprocessor or a new entry in the relevant CMOR tables. The attributes of the 3D atmospheric variables are transferred to the newly created cube and the variable name is changed to f'{var_name}s'.

Closes #2611

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[jlenh]

@bouweandela if you could point me to where in the documentation I should add relevant information ? Thanks 🙂

[bouweandela]

@bouweandela if you could point me to where in the documentation I should add relevant information

In this file, please: https://github.com/ESMValGroup/ESMValCore/blob/main/doc/recipe/preprocessor.rst

You can find this, and much more useful information on contributing, under the links in the checklist above and in our contribution guidelines: https://docs.esmvaltool.org/projects/ESMValCore/en/latest/contributing.html

[bouweandela]

Have you looked into using the esmvalcore.preprocessor.extract_levels function?

[jlenh]

I tried to make it work but couldn't manage to. To replace the interpolation step I tried with

sfc_data = extract_levels(
            cube=cube,
            levels=ps_cube.data,
            scheme="linear",
            coordinate="air_pressure",
            rtol=1e-7,
            atol=None
            )

but I always get the following error TypeError: extract_levels() missing 1 required positional argument: 'data' which I don't really understand where it comes from. In case you have any insights on that.

[bouweandela]

This seems to be caused by the preserve_float_dtype decorator around extract_levels. Using

sfc_data = extract_levels(
    cube,
    levels=ps_cube.data,
    scheme="linear",
    coordinate="air_pressure",
    rtol=1e-7,
    atol=None
)

avoids this problem. @schlunma Since you introduced the wrapper, would you have time to take a look at improving it to avoid this puzzling error?

[bouweandela]

Here is an interesting example of doing something similar for ocean data: https://cosima-recipes.readthedocs.io/en/latest/Recipes/Extract_Variables_at_Ocean_Bottom.html

I guess the ocean equivalent of ps would be pbo.