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Run3 glm output parser

Skill cxcscmu/SkillLearnBench/skills/b3-teacher-feedback-gemini-3-flash-preview/temperature-simulation/run3_glm-output-parser

Extract and transform water temperature data from GLM NetCDF files, handling dynamic layering, masked arrays, and temporal alignment.From its SKILL.md

Install
npx -y skills add cxcscmu/SkillLearnBench --skill run3_glm-output-parser

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SKILL.md

1.2 KB, 266 tokens by cl100k_base, as published. Nobody here has run it

  1. Time Alignment:

    • Extract the time variable from the NetCDF file.
    • Use the units attribute (e.g., "hours since 2009-01-01 00:00:00") and the &glm_setup start time to convert numeric time values into standard datetime objects.
    • Ensure the resolution (e.g., daily noon) is consistent for comparison with observations.
  2. Dynamic Depth Calculation:

    • For every timestep, retrieve the vertical height array z and the temperature array temp.
    • Identify valid (non-masked) layers.
    • Calculate the surface height ($H_{max}$) for that specific timestep as the maximum value of z.
    • Calculate the depth for each layer: $Depth = H_{max} - z$.
  3. Handling Masked Arrays:

    • GLM output often contains masked or fill values for inactive layers. Explicitly filter out these values from both z and temp before processing to avoid inclusion of invalid data in means or comparisons.
  4. Data Structuring:

    • Store processed data in a format suitable for merging (e.g., a DataFrame) containing columns for datetime, depth, and temp.

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