Feature dtcenter/METbaseimage#61 python 3.14 conda envs

[georgemccabe]

Part of work for dtcenter/METbaseimage#61

Pull Request Testing

• Describe testing already performed for these changes:
  

Ran use cases, then debugged issues with failing use cases relating to pandas 2>3

• Recommend testing for the reviewer(s) to perform, including the location of input datasets, and any additional instructions:
  

@jprestop : Review changes to conda environment files for your own understanding

Use case owners: Please review changes to use case python scripts for your awareness and differences in the output (if any).

@DanielAdriaansen :

• land/PointStat_fcstUFS_obsGDAS_CTP_HI/create_raob_mask_file.py

@CPKalb

• s2s_mjo/common/OMI_driver.py
• s2s_mjo/UserScript_obsERA_obsOnly_PhaseDiagram/PhaseDiagram_driver.py
• s2s_stratosphere/UserScript_fcstGFS_obsERA_StratospherePolar/bias_rmse_plot_driver.py
• fire/PointStat_fcstHRRR_obsMADIS_FrazierFire (differences in output)

@willmayfield

• unstructured_grids/StatAnalysis_fcstLFRIC_UGRID_obsASCII_PyEmbed/ugrid_lfric_mpr.py

• Do these changes include sufficient documentation updates, ensuring that no errors or warnings exist in the build of the documentation? [Yes]

• Do these changes include sufficient testing updates? [Yes]

• Will this PR result in changes to the test suite? [No]
  
  If yes, describe the new output and/or changes to the existing output:
  

• Do these changes introduce new SonarQube findings? [No]
  
  If yes, please describe:

• Please complete this pull request review by 5/4/2026.
  

Pull Request Checklist

See the METplus Workflow for details.

• Add any new Python packages to the METplus Components Python Requirements table.
• For any new datasets, an entry to the METplus Verification Datasets Guide.
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• Complete the PR definition above.
• Ensure the PR title matches the feature or bugfix branch name.
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  Select: Reviewer(s) and Development issue
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[coveralls]

Coverage Report for CI Build 25222239880

Coverage remained the same at 92.054%

Details

• Coverage remained the same as the base build.
• Patch coverage: No coverable lines changed in this PR.
• No coverage regressions found.

Uncovered Changes

No uncovered changes found.

Coverage Regressions

No coverage regressions found.

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Coverage Stats

Relevant Lines:	10471
Covered Lines:	9639
Line Coverage:	92.05%
Coverage Strength:	1.84 hits per line

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💛 - Coveralls

[georgemccabe]

The differences in these outputs are negligible image diffs:

medium_range:12 -- medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime
s2s_mid_lat:0-2 -- s2s_mid_lat/UserScript_obsERA_obsOnly_WeatherRegime
clouds:8 -- clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer
land:2 -- land/PointStat_fcstUFS_obsISMN_SoilMoistureTemp (map is shifted slightly north but points are in the same locations)

The following have rounding differences that come from metpy functions that can produce slightly different results

medium_range:8 -- medium_range/TCStat_SeriesAnalysis_fcstGFS_obsGFS_FeatureRelative_SeriesByLead_PyEmbed_Multiple_Diagnostics
land:1 -- land/PointStat_fcstUFS_obsGDAS_CTP_HI

[CPKalb]

Examined the differences. All of the changes I saw result either from small changes in Metpy functions, or negligible image differences.

[jprestop]

Thanks for all of your work on this @georgemccabe and for including me in the PR. I have reviewed changes to the conda environment files.

[DanielAdriaansen]

I talked with @georgemccabe offline on several of the differences including from the CTP/HI model_applications/land use case and the medium_range multiple diagnostic use case computing Saturation Equivalent Potential Temperature (sept). These use cases utilize MetPy and Python embedding to perform calculations, and the differences observed in the output from these use cases changed but in a very small way relative to the magnitude of typical values for the fields being computed. It's hard to fully track down where the differences are introduced, as it could be from an updated version of MetPy, some of its dependencies, or some other dependencies within the Python installation. For the CTP/HI use case, an iterative solver is used during the computation of the moist lapse rate, which could be an obvious area where differences could originate but we did not track it down explicitly. We feel the differences were small enough to approve updating the baseline data.