Extracting surface level data from TROPOMI Tropospheric column using HARP

[Nad24]

Hello everyone,
I’m working with TROPOMI data (CH4) and I need to extract only the surface level data from the full tropospheric column information available in the Level 2 files. Is there a HARP operation or a combination of operations that would allow me to do this easily?

I am working with TROPOMI CH4 L2 data products in jupyterlab and would appreciate any guidance or code examples you can provide.
Thanks in advance for your help!

[sander.niemeijer]

There is unfortunately no easy way to do this.
See also similar posts:

Convert the unit from molecules/m^2 to ppm

This is an inherent limitation of the way the satellite can measure the atmosphere. You won’t be able to retrieve parts of a vertical column. See also this earlier comment. The way the S5P NO2 L2 product is able to split a total column in a tropospheric and stratospheric column is already quite an achievement (and requires the use of an atmospheric model to achieve it).

and:

Derive PPB/PPM of tropospheric NO2

Good morning D, There is no basic knowledge unless you have mastered it first! So don’t worry at all about asking questions, this forum is the best place for it, no judgements and Sander is excellent in replying to all bizarre questions we pose him. I think there is a slight confusion with the units in your question. You mention the question I posed here, S5P CH4 | Column ppb to column number density mol/m2 conversion, which however relates to how the CH4 product is being retrieved from the da…

[Nad24]

Thank you for the response, Sander. Is there any other way to do it?
I went through the HARP operations and came across some vertical smoothening operations. what exactly are those for?

[sander.niemeijer]

Vertical smoothing is about the application of the averaging kernel. You would apply this to a vertical profile of a model or of an accurate ground based observation so the profile will resemble how the satellite instrument would have seen it.

There are two aspects to this. One is that the satellite is not equally sensitive to each layer in the atmosphere. So the satellite may not see everything that is in the atmosphere. Where the satellite is not sensitive, the information will come primarily from the apriori profile that was used in the retrieval.
The other aspect is that the satellite will not be able to see the vertical distribution of a quantity at high vertical resolution. It sees a very ‘blurred’ version of this. The 2D averaging kernel gives information on this blurriness.

Then again, I am not an atmospheric scientist, and there are probably other aspects to this. But this is my layman understanding of it.

[Nad24]

Thanks very much for the explanation.
I guess I’ll have to find a different way to approach my research then.