glm-calibration

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

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

python

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