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The first UCLALES-SALSA results #48

Merged
merged 4 commits into from
Apr 11, 2024
Merged

The first UCLALES-SALSA results #48

merged 4 commits into from
Apr 11, 2024

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TomiRaatikainen
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The first UCLALES-SALSA results for Part I can be found from output_les/uclales-salsa/sandbox/. These were calculated using notebook convert_UCLALES-SALSA_output_to_dephy_format.ipynb from notebooks/conversion_output/. Model inputs were generated using notebook convert_comble_dephy_forcing_to_UCLALES-SALSA_forcing.ipynb from notebooks/conversion/. Model inputs or the raw output data are not included.

TomiRaatikainen and others added 4 commits March 15, 2024 10:30
Generated notebooks for producing UCLALES-SALSA inputs and dephy
outputs. The first dephy outputs are also produced and tested.

 On branch UCLALES-SALSA
 Changes to be committed:
	new file:   notebooks/conversion/convert_comble_dephy_forcing_to_UCLALES-SALSA_forcing.ipynb
	new file:   notebooks/conversion_output/convert_UCLALES-SALSA_output_to_dephy_format.ipynb
	new file:   output_les/uclales-salsa/sandbox/UCLALES-SALSA_Lx25_dx100_FixN.nc
	new file:   output_les/uclales-salsa/sandbox/UCLALES-SALSA_Lx25_dx100_FixN_def_z0.nc
	new file:   output_les/uclales-salsa/sandbox/UCLALES-SALSA_Lx25_dx100_FixN_noice.nc
 On branch UCLALES-SALSA
 Changes to be committed:
	modified:   notebooks/plotting/check_les.ipynb
@twjuliano twjuliano merged commit 0af6aad into main Apr 11, 2024
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@twjuliano
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@TomiRaatikainen It looks like the 'FixN' and 'FixN_def_z0' files don't contain 2D information through the end of the simulation. Feel free to submit another PR that includes those fields. Thanks.

@TomiRaatikainen
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@TomiRaatikainen It looks like the 'FixN' and 'FixN_def_z0' files don't contain 2D information through the end of the simulation. Feel free to submit another PR that includes those fields. Thanks.

I looked at the files and the data seemed to be there. However, the output model time varies depending on the CFL condition. In practice, the model time may exceed the target time by 1 s (the maximum time step). The plotting script (check_les.ipynb) uses condition df = df[round(df.time) == times[tt]*3600.], which means that the maximum difference is 0.5 s. The simplest fix to the problem is to change the condition to df = df[abs(round(df.time)-times[tt]*3600.)<2.]. Would this be enough for now? I can also update the data.

@twjuliano
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@TomiRaatikainen I see, thanks for explaining. This is something we haven't thought about yet. @fridlind @floriantornow Any thoughts on how we should handle this?

@fridlind
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@TomiRaatikainen Yes, understood, our model does this, too. What I do is report the nearest appropriate value on the requested output time frequency, which is for all practical purposes very close as in your explanation above. If you don't mind updating your times to be precisely the requested frequency, just to simplify things on our end, that would be much appreciated, but yes, we can work with this.

@floriantornow Can you please relax the check_les.ipynb rounding threshold as suggested above, in any case?

Many thanks all around!

@floriantornow
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@fridlind @twjuliano Yes, done! Thank you for the suggestion, @TomiRaatikainen ! I changed the function as suggested.

@twjuliano
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@fridlind @twjuliano Yes, done! Thank you for the suggestion, @TomiRaatikainen ! I changed the function as suggested.

Thanks all! @floriantornow @fridlind @TomiRaatikainen

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4 participants