Add Hovmoeller MJO diagnostic - #4564
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Thanks @ApexCapybara @JurijSchoenfeld (copy-paste fail 🤦 ), looks great already! Just a couple of comments to make the code simpler. It would be great if you could add documentation to the recipe/diagnostic (e.g., like done in #3155).
If you want, you can also add your name to the CITATION.cff and .zenodo.json to become an official contributor.
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Hi @schlunma I pushed my commits containing all your feedback. I also resolved most of commit autoreview comments except the code style warnings from Codacy that I find a bit pedantic. Please let me know if you have further questions or need me to change anything :) |
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Thanks @JurijSchoenfeld, that's great! I added a couple of commits with automatic fixes, which got rid of almost all Codacy warnings. The only thing that's missing is a little bit of documentation. You could take a look at #4561 and do it the same way (focus on the files in |
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Hi @schlunma, I added some documentation and used the template.rst file as a reference. Let me know if you need further changes |
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Fantastic, thanks so much @JurijSchoenfeld! It might make sense to generalize the docs within #3995 once this is ready for review, but for now this looks good to me!
@ESMValGroup/atmosphere would anyone be able to do a quick science review of this? On the surface this looks good to me, but I am certainly no expert on this topic. Thanks so much!
Example output is available here: https://swift.dkrz.de/v1/dkrz_4eefb34f-8803-415a-bd70-9c455db9a403/esmvaltool_output/mjo_jurij/index.html
LisaBock
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Thanks @JurijSchoenfeld !
Looks mostly fine to me. I have only two small comments.
| contour_levels: 21 | ||
| colormap: RdYlBu | ||
| plot_title: MJO precipitation Hovmöller diagram | ||
| colorbar_label: Precipitation regression (kg m-2 day-1) |
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Maybe you can write here "mm day-1" as units in the brackets. We use this typically for precipitation.
| longitude = longitude[order] | ||
| regression = regression[:, order] | ||
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| vmax = np.ma.max(np.ma.abs(regression)) |
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It would be useful if you would be able to set vmax also in the recipe. This enables an easier comparison between model and observation.
Description
Evaluating atmospheric patterns like the MJO, is important to assess the performance of an ESM. Applications of this diagnostic include but are not limitted to evaluation of different convective parameterizations. The diagnostic adds a standard metric to diagnose MJO, which is not straight forward to compute, involving a (slightly) tricky filter operation.
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