# Glm Output

> Read and process GLM output files. Use when you need to extract temperature data from NetCDF output, convert depth coordinates, or calculate RMSE against observations. Use when this capability is needed.

- Skill: `tomevault-io/glm-output` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/glm-output`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/glm-output/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/glm-output

---


# GLM Output Guide

## Overview

GLM produces NetCDF output containing simulated water temperature profiles. Processing this output requires understanding the coordinate system and matching with observations.

## Output File

After running GLM, results are in `output/output.nc`:

| Variable | Description | Shape |
|----------|-------------|-------|
| `time` | Hours since simulation start | (n_times,) |
| `z` | Height from lake bottom (not depth!) | (n_times, n_layers, 1, 1) |
| `temp` | Water temperature (°C) | (n_times, n_layers, 1, 1) |

## Reading Output with Python
```python
from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

nc = Dataset('output/output.nc', 'r')
time = nc.variables['time'][:]
z = nc.variables['z'][:]
temp = nc.variables['temp'][:]
nc.close()
```

## Coordinate Conversion

**Important**: GLM `z` is height from lake bottom, not depth from surface.
```python
# Convert to depth from surface
# Set LAKE_DEPTH based on lake_depth in &init_profiles section of glm3.nml
LAKE_DEPTH = <lake_depth_from_nml>
depth_from_surface = LAKE_DEPTH - z
```

## Complete Output Processing
```python
from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

def read_glm_output(nc_path, lake_depth):
    nc = Dataset(nc_path, 'r')
    time = nc.variables['time'][:]
    z = nc.variables['z'][:]
    temp = nc.variables['temp'][:]
    start_date = datetime(2009, 1, 1, 12, 0, 0)

    records = []
    for t_idx in range(len(time)):
        hours = float(time[t_idx])
        date = pd.Timestamp(start_date) + pd.Timedelta(hours=hours)
        heights = z[t_idx, :, 0, 0]
        temps = temp[t_idx, :, 0, 0]

        for d_idx in range(len(heights)):
            h_val = heights[d_idx]
            t_val = temps[d_idx]
            if not np.ma.is_masked(h_val) and not np.ma.is_masked(t_val):
                depth = lake_depth - float(h_val)
                if 0 <= depth <= lake_depth:
                    records.append({
                        'datetime': date,
                        'depth': round(depth),
                        'temp_sim': float(t_val)
                    })
    nc.close()

    df = pd.DataFrame(records)
    df = df.groupby(['datetime', 'depth']).agg({'temp_sim': 'mean'}).reset_index()
    return df
```

## Reading Observations
```python
def read_observations(obs_path):
    df = pd.read_csv(obs_path)
    df['datetime'] = pd.to_datetime(df['datetime'])
    df['depth'] = df['depth'].round().astype(int)
    df = df.rename(columns={'temp': 'temp_obs'})
    return df[['datetime', 'depth', 'temp_obs']]
```

## Calculating RMSE
```python
def calculate_rmse(sim_df, obs_df):
    merged = pd.merge(obs_df, sim_df, on=['datetime', 'depth'], how='inner')
    if len(merged) == 0:
        return 999.0
    rmse = np.sqrt(np.mean((merged['temp_sim'] - merged['temp_obs'])**2))
    return rmse

# Usage: get lake_depth from glm3.nml &init_profiles section
sim_df = read_glm_output('output/output.nc', lake_depth=25)
obs_df = read_observations('field_temp_oxy.csv')
rmse = calculate_rmse(sim_df, obs_df)
print(f"RMSE: {rmse:.2f}C")
```

## Common Issues

| Issue | Cause | Solution |
|-------|-------|----------|
| RMSE very high | Wrong depth conversion | Use `lake_depth - z`, not `z` directly |
| No matched observations | Datetime mismatch | Check datetime format consistency |
| Empty merged dataframe | Depth rounding issues | Round depths to integers |

## Best Practices

- Check `lake_depth` in `&init_profiles` section of `glm3.nml`
- Always convert z to depth from surface before comparing with observations
- Round depths to integers for matching
- Group by datetime and depth to handle duplicate records
- Check number of matched observations after merge

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->