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 \x3C 2.0°C.

Key Calibration Parameters

Parameter Effects

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 \x3C 2.0°C

Common Issues

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 \x3C 2.0°C for good calibration