Run2 glm error diagnosis

GLM Error Diagnosis and Debugging

Common Issues and Diagnosis

Issue 1: Uniform Temperature Profiles (All Depths Same Temp)

Symptom: Simulation shows constant temperature across all depths

Causes:

1. Simulation not initialized properly: Uniform initial conditions applied incorrectly
2. Time step too early: Simulation still evolving from initial state
3. Broken forcing data: If meteorological forcing is constant

Diagnosis:

import netCDF4 as nc
import numpy as np

ds = nc.Dataset(output_file, 'r')
temp_data = ds.variables['temp'][0, :, 0, 0]  # First time step, first lat/lon
z_data = ds.variables['z'][0, :, 0, 0]

valid_indices = np.where(~np.isnan(temp_data))[0]
unique_temps = np.unique(temp_data[valid_indices])

print(f"Unique temperatures: {len(unique_temps)}")
print(f"All same? {len(unique_temps) == 1}")
print(f"Temperature range: {temp_data.min()}-{temp_data.max()}")

Fix: Check initialization profile in NML and verify it represents actual starting state

Issue 2: Temperature Offset (Simulation Always Too Warm or Too Cold)

Symptom: RMSE constant across all depths, bias consistent (all simulated temps are X°C higher/lower)

Diagnosis:

def analyze_temperature_bias(merged_df):
    """Compute bias in simulation"""
    diff = merged_df['sim_temp'] - merged_df['obs_temp']

    print(f"Mean bias: {diff.mean():.2f}°C")
    print(f"Std bias: {diff.std():.2f}°C")
    print(f"Bias by depth:")

    for depth in sorted(merged_df['depth_rounded'].unique()):
        depth_data = diff[merged_df['depth_rounded'] == depth]
        print(f"  Depth {depth:2d}m: {depth_data.mean():+.2f}°C")

Causes:

1. Initial conditions too warm/cold
2. All forcing data scaled wrong (lw_factor, sw_factor)
3. All forcing data systematically wrong

Fix:

• If too warm: Increase ch (heat loss), decrease lw_factor, or decrease Kw
• If too cold: Decrease ch, increase lw_factor, or increase Kw

Issue 3: Stratification Wrong (Depth Variation Wrong)

Symptom: Mean temperature might be OK, but stratification RMSE is large

Diagnosis:

def analyze_stratification(merged_df):
    """Check if depth gradients match"""

    for t in sorted(merged_df['datetime'].unique())[:5]:  # Check first 5 times
        at_time = merged_df[merged_df['datetime'] == t]
        at_time = at_time.sort_values('depth_rounded')

        print(f"\nTime {t}:")
        for _, row in at_time.head(15).iterrows():
            d = row['depth_rounded']
            o = row['obs_temp']
            s = row['sim_temp']
            diff = s - o
            print(f"  Depth {d:2d}m: obs={o:5.1f}  sim={s:5.1f}  diff={diff:+.1f}")

Causes:

1. coef_mix_hyp wrong: Too much/little deep water mixing
2. wind_factor wrong: Surface mixing not capturing dynamics
3. Kw wrong: Light penetration affects density structure

Fix: Adjust mixing parameters based on whether deep water is too warm or too cold

Issue 4: Seasonal Pattern Wrong

Symptom: Summer RMSE >> Winter RMSE (or vice versa)

Diagnosis:

def analyze_by_season(merged_df):
    """Decompose error by season"""
    merged_df['month'] = merged_df['datetime'].dt.month

    for season_name, months in [('Winter', [12,1,2]), ('Spring', [3,4,5]),
                                 ('Summer', [6,7,8]), ('Fall', [9,10,11])]:
        season_data = merged_df[merged_df['month'].isin(months)]
        if len(season_data) > 0:
            rmse = np.sqrt(np.mean((season_data['sim_temp'] - season_data['obs_temp'])**2))
            bias = (season_data['sim_temp'] - season_data['obs_temp']).mean()
            print(f"{season_name:6s}: RMSE={rmse:.2f}°C, bias={bias:+.2f}°C ({len(season_data)} pairs)")

Common patterns:

• Summer too warm, winter OK: Kw too high (too much light) or coef_mix_hyp too low (weak mixing)
• Summer OK, winter too cold: Initial conditions wrong or winter forcing data issue
• Spring/Fall transitions bad: Mixing parameters (wind_factor, coef_mix_hyp) likely wrong

Issue 5: Deep Water Specifically Bad

Symptom: overall_rmse OK, annual_deep_rmse >> threshold

Diagnosis:

def analyze_by_depth(merged_df):
    """Decompose error by depth band"""

    for depth_range in [(0, 5), (5, 10), (10, 15), (15, 20), (20, 25)]:
        band_data = merged_df[(merged_df['depth_rounded'] >= depth_range[0]) &
                              (merged_df['depth_rounded'] < depth_range[1])]
        if len(band_data) > 0:
            rmse = np.sqrt(np.mean((band_data['sim_temp'] - band_data['obs_temp'])**2))
            print(f"Depth {depth_range[0]:2d}-{depth_range[1]:2d}m: RMSE={rmse:.2f}°C ({len(band_data):4d} pairs)")

Causes:

1. coef_mix_hyp controls deep mixing: Wrong value = wrong deep stratification
2. Kw affects light penetration to depth: Wrong value = wrong density structure

Fix:

• If deep water too warm: Increase coef_mix_hyp (more mixing brings cold water up)
• If deep water too cold: Decrease coef_mix_hyp (less mixing keeps it isolated)

Data Quality Checks

Check Observation Distribution

def check_obs_distribution(obs_df):
    """Ensure observations span full lake and season"""

    print(f"Depth range: {obs_df['depth'].min()}-{obs_df['depth'].max()}m")
    print(f"Time span: {obs_df['datetime'].min()} to {obs_df['datetime'].max()}")
    print(f"Samples per depth:")

    for depth in sorted(obs_df['depth'].unique()):
        count = len(obs_df[obs_df['depth'] == depth])
        print(f"  {depth:5.1f}m: {count:3d} samples")

Check Simulation Range

def check_sim_range(temp_data, z_data):
    """Verify simulation outputs realistic values"""

    valid_temps = temp_data[~np.isnan(temp_data)]

    print(f"Temperature range: {valid_temps.min():.1f} to {valid_temps.max():.1f}°C")
    print(f"Mean: {valid_temps.mean():.1f}°C")

    # Most lakes should be 0-30°C
    if valid_temps.min() < -10 or valid_temps.max() > 40:
        print("WARNING: Unrealistic temperature range!")

    # Check depth range
    valid_z = z_data[~np.isnan(z_data)]
    print(f"Depth range: {valid_z.min():.1f} to {valid_z.max():.1f}m")

Iterative Refinement

1. Compute detailed metrics - which metric fails most?
2. Analyze spatiotemporal pattern - which times/depths are worst?
3. Identify primary error - bias, stratification, seasonal, or depth-specific?
4. Propose parameter change - based on physical reasoning
5. Test change - evaluate effect on all metrics
6. Repeat until success

Each iteration should reduce RMSE, not increase it. If change makes things worse, revert and try different parameter.