glm-output

Extract GLM NetCDF temperature data and align by depth and time.

Updated Jan 15, 2026

Search Tags:#temperature #data-processing #glm #netcdf #depth-conversion #rmse #lake-model

Ready to run

npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill glm-output

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: glm-output
Source: https://github.com/KaiserWhoLearns/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-output
Command: npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill glm-output

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Read and process GLM NetCDF output files to extract temperature data and align it with depth and time for validation and analysis.

Core Features & Use Cases

Read NetCDF GLM outputs and extract time, depth, and temperature data.
Convert z coordinates to depth and compute depth-aware statistics (e.g., RMSE against observations).
Generate ready-to-use data frames for comparison with field measurements.

Quick Start

Load GLM output, extract temperatures by depth, and compute RMSE against available 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 files?▼

You can extract temperature profiles from GLM NetCDF output by reading the file directly, accessing the time and z-coordinate variables, and converting the z-axis values to standard depth measurements to align temperature data by depth and time.

How do I convert GLM z-coordinates to depth for lake model validation?▼

To convert GLM z-coordinates to depth for lake model validation, you apply a depth conversion transformation to the vertical axis data, which aligns the modeled temperatures with observed field measurements taken at specific physical depths in the lake.

Can I compute RMSE for lake model temperatures against field observations?▼

Yes, you can compute RMSE for lake model temperatures by aggregating the extracted NetCDF temperature data by datetime and depth, cleaning any masked values, and comparing the resulting data frame directly against observed field measurements.

What is the best way to clean masked values when processing NetCDF lake data?▼

The best way to clean masked values in NetCDF lake data is to implement a data cleaning step that identifies and removes or ignores masked entries during the aggregation process, ensuring that only valid temperature data points are used for RMSE computation.

Does this approach require dependencies for reading NetCDF files?▼

No specific dependencies are required, as the processing logic implements its own functionality for reading NetCDF files, converting depth, and aggregating temperature data by datetime and depth without external Skill dependencies.