glm-calibration

Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE between simulated and observed tempe...

Security Scan

The guide describes reasonable GLM calibration steps, but the runtime instructions implicitly require local binaries, Python packages, and input/output files that are not declared — the mismatch and the instruction to run/modify local files deserve caution.

Purpose & Capability

The skill's stated purpose (calibrating GLM) is coherent with the SKILL.md content. However, the instructions assume the presence of an external 'glm' executable, a local configuration file (glm3.nml), and Python dependencies (scipy, subprocess usage, dataframes sim_df/obs_df) while the manifest declares no required binaries, env vars, or config paths. The omission of these required runtime artifacts is an inconsistency.

Instruction Scope

The SKILL.md explicitly instructs modifying glm3.nml and running the 'glm' binary via subprocess, as well as computing RMSE from sim_df and obs_df. Those actions are within the stated calibration purpose, but the instructions don't say where obs_df/sim_df come from, how to ensure backups, or that the glm binary will be executed with what inputs. Running an unmanaged external binary and editing a local .nml file are notable runtime effects that should be documented.

Install Mechanism

This is an instruction-only skill with no install spec or downloaded code, which minimizes supply-chain risk. There is no installer or archive to review.

Credentials

No credentials or environment variables are requested (appropriate). However, the instructions implicitly require filesystem read/write access (glm3.nml) and execution permission for a local 'glm' binary, plus a Python environment with SciPy and dataframes. Those implicit requirements are reasonable for calibration but should have been declared in the manifest so users know what privileges and tooling are needed.

Persistence & Privilege

The skill does not request always: true and is not trying to persist or modify other skills or system-wide settings. Autonomous invocation is enabled by default on the platform (not a problem by itself), but note that the instructions will cause the agent to run local binaries and edit files when invoked.

What to consider before installing

This skill's guide appears to be a legitimate how-to for calibrating GLM, but it omits key runtime details. Before installing or enabling it: 1) Confirm you have the GLM executable available and trust its origin; the skill will call subprocess.run(['glm']) and execute that binary. 2) Ensure you have a Python environment with scipy and whatever code provides modify_nml, calculate_rmse, and the sim_df/obs_df data structures; the SKILL.md references these but doesn't provide implementations. 3) Back up your glm3.nml (and any other model input files) — the instructions modify that file. 4) If you don't want an agent to run binaries or edit files on your machine autonomously, disable autonomous invocation for this skill or run it in a sandbox. 5) Prefer a version of the skill that declares required binaries/dependencies and explains where observation data should come from. If you need higher assurance, request the author provide: a) an explicit manifest of required tools and files, b) example scripts for prepare/load obs_df and sim_df, and c) instructions for safe execution (dry-run, backups, logging).

GLM Calibration Guide

Overview

GLM calibration involves adjusting physical parameters to minimize the difference between simulated and observed water temperatures. The goal is typically to achieve RMSE < 2.0°C.

Key Calibration Parameters

Parameter	Section	Description	Default	Range
Kw	&light	Light extinction coefficient (m⁻¹)	0.3	0.1 - 0.5
coef_mix_hyp	&mixing	Hypolimnetic mixing coefficient	0.5	0.3 - 0.7
wind_factor	&meteorology	Wind speed scaling factor	1.0	0.7 - 1.3
lw_factor	&meteorology	Longwave radiation scaling	1.0	0.7 - 1.3
ch	&meteorology	Sensible heat transfer coefficient	0.0013	0.0005 - 0.002

Parameter Effects

Parameter	Increase Effect	Decrease Effect
Kw	Less light penetration, cooler deep water	More light penetration, warmer deep water
coef_mix_hyp	More deep mixing, weaker stratification	Less mixing, stronger stratification
wind_factor	More surface mixing	Less surface mixing
lw_factor	More heat input	Less heat input
ch	More sensible heat exchange	Less heat exchange

Calibration with Optimization

from scipy.optimize import minimize

def objective(x):
    Kw, coef_mix_hyp, wind_factor, lw_factor, ch = x

    # Modify parameters
    params = {
        'Kw': round(Kw, 4),
        'coef_mix_hyp': round(coef_mix_hyp, 4),
        'wind_factor': round(wind_factor, 4),
        'lw_factor': round(lw_factor, 4),
        'ch': round(ch, 6)
    }
    modify_nml('glm3.nml', params)

    # Run GLM
    subprocess.run(['glm'], capture_output=True)

    # Calculate RMSE
    rmse = calculate_rmse(sim_df, obs_df)
    return rmse

# Initial values (defaults)
x0 = [0.3, 0.5, 1.0, 1.0, 0.0013]

# Run optimization
result = minimize(
    objective,
    x0,
    method='Nelder-Mead',
    options={'maxiter': 150}
)

Manual Calibration Strategy

1. Start with default parameters, run GLM, calculate RMSE
2. Adjust one parameter at a time
3. If surface too warm → increase wind_factor
4. If deep water too warm → increase Kw
5. If stratification too weak → decrease coef_mix_hyp
6. Iterate until RMSE < 2.0°C

Common Issues

Issue	Likely Cause	Solution
Surface too warm	Low wind mixing	Increase wind_factor
Deep water too warm	Too much light penetration	Increase Kw
Weak stratification	Too much mixing	Decrease coef_mix_hyp
Overall warm bias	Heat budget too high	Decrease lw_factor or ch

Best Practices

• Change one parameter at a time when manually calibrating
• Keep parameters within physical ranges
• Use optimization for fine-tuning after manual adjustment
• Target RMSE < 2.0°C for good calibration