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glm-output

Extract GLM NetCDF temperature profiles and convert z coordinates to depth.

4|Updated Feb 22, 2026
Search Tags:#temperature#glm#netcdf#depth-conversion#rmse#lake-model
One-click install
npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill glm-output-generalreasoning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: glm-output
Source: https://github.com/GeneralReasoning/env-skillsbench/tree/main/glm-lake-mendota/environment/skills/glm-output
Command: npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill glm-output-generalreasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

GLM outputs are NetCDF files that contain time-varying temperature profiles mapped to lake depth. This skill provides a clear workflow to read the NetCDF, align coordinates, and produce comparable temperature series.

Core Features & Use Cases

  • Read GLM NetCDF outputs with Python libraries (netCDF4, numpy, pandas)
  • Convert GLM z coordinates to depth using the lake depth parameter
  • Align timestamps and depths with observations for RMSE evaluation
  • Compute RMSE between simulated and observed temperatures across time and depth
  • Example: generate a depth-resolved RMSE time series for a field campaign

Quick Start

Run the glm-output workflow on your NetCDF file to extract temperature profiles, convert depth from lake bottom, and compute RMSE against observations.

Frequently Asked Questions about glm-output

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract temperature profiles from GLM NetCDF output for lake-model validation?▼

This Skill processes GLM NetCDF outputs by reading the file with Python libraries and converting the GLM 'z' coordinate to actual depth, producing comparable temperature time series for lake-model validation.

How do I convert GLM z coordinates to lake depth for time-depth RMSE evaluation?▼

You can convert GLM z coordinates to depth by applying the lake depth parameter within the Python workflow, aligning simulated temperatures with observed data for accurate time-depth RMSE evaluation.

Can I compute RMSE between simulated and observed temperatures across time and depth using pandas and numpy?▼

Yes, this workflow uses pandas and numpy to align timestamps and depths between simulated and observed temperatures, computing RMSE across time and depth to support field campaign analysis.

What's the best way to align GLM NetCDF timestamps with observation data?▼

The recommended approach uses pandas to align GLM NetCDF timestamps and depths with field observations, ensuring simulated and observed temperature series are directly comparable before computing RMSE.

Do I need specific Python libraries to process GLM NetCDF files and calculate RMSE?▼

Yes, you need the netCDF4, numpy, and pandas Python libraries to read GLM NetCDF files, perform depth conversion, and compute RMSE against observed temperature data.

When do I need to convert GLM z coordinates to depth for lake temperature analysis?▼

You need to convert GLM z coordinates to depth when comparing simulated lake temperature profiles against field observations, because aligning vertical coordinates is required for accurate RMSE evaluation.

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