Run3 glm basics

Overview

Before calibrating GLM parameters, establish a baseline understanding of:

• The configuration file structure and current parameter values
• Available input data and their formats
• The observation dataset that will be used for validation

Steps

1. Inspect the GLM Configuration File

head -100 /root/glm3.nml

Look for the &calibration section and note the current values of:

• Kw, coef_mix_hyp, wind_factor, lw_factor, ch

Also confirm that sw_factor, cd, ce, initialization profile (the_depths, the_temps, the_sals), and simulation dates are unchanged.

2. Read Current Calibration Parameters

Create a Python script to extract current parameter values robustly:

import re

def read_current_nml_parameters(nml_path, param_names):
    """
    Extract calibration parameters from GLM .nml file.
    
    Handles:
    - Parameters at line start or after whitespace
    - Scientific notation (e.g., 1.0e-4, 1e-3)
    - Parameters with or without leading/trailing spaces
    
    Returns dict of {param: float_value}, or raises ValueError if any param missing.
    """
    pattern_template = r'{param}\s*=\s*([\d.]+(?:[eE][+-]?\d+)?)'
    
    params = {}
    with open(nml_path, 'r') as f:
        content = f.read()
    
    for param in param_names:
        pattern = pattern_template.format(param=param)
        match = re.search(pattern, content)
        if not match:
            raise ValueError(f"Parameter '{param}' not found in {nml_path}")
        params[param] = float(match.group(1))
    
    print(f"Successfully read {len(params)} parameters from {nml_path}:")
    for p, v in params.items():
        print(f"  {p} = {v}")
    
    return params

# Test extraction
current_params = read_current_nml_parameters(
    '/root/glm3.nml',
    ['Kw', 'coef_mix_hyp', 'wind_factor', 'lw_factor', 'ch']
)

3. Verify Input Data Availability

import os
import pandas as pd

# Check meteorological/hydrological forcing
bcs_path = '/root/bcs/'
if os.path.isdir(bcs_path):
    files = os.listdir(bcs_path)
    print(f"Found {len(files)} forcing files in {bcs_path}")
    print(f"Files: {sorted(files)}")
else:
    raise FileNotFoundError(f"{bcs_path} not found")

# Check observation data
obs_path = '/root/field_temp_oxy.csv'
if os.path.exists(obs_path):
    obs_df = pd.read_csv(obs_path)
    print(f"\nObservation file: {obs_path}")
    print(f"Shape: {obs_df.shape}")
    print(f"Columns: {list(obs_df.columns)}")
    print(f"Date range: {obs_df.iloc[0:5]}")
else:
    raise FileNotFoundError(f"{obs_path} not found")

# Check output directory
output_dir = '/root/output'
if os.path.isdir(output_dir):
    print(f"\nOutput directory exists: {output_dir}")
else:
    os.makedirs(output_dir, exist_ok=True)
    print(f"Created output directory: {output_dir}")

4. Validate Calibration Parameter Ranges

def validate_ranges(params, ranges):
    """
    Check that all parameters fall within published calibration ranges.
    
    Args:
        params (dict): Parameter name → value
        ranges (dict): Parameter name → (min, max) tuple
    
    Returns:
        bool: True if all params in range
    
    Raises:
        ValueError: If any param out of range
    """
    for param, value in params.items():
        if param in ranges:
            min_val, max_val = ranges[param]
            if not (min_val <= value <= max_val):
                raise ValueError(
                    f"{param}={value} is outside range [{min_val}, {max_val}]"
                )
    print("All parameters are within published calibration ranges.")
    return True

ranges = {
    'Kw': (0.1, 0.5),
    'coef_mix_hyp': (0.3, 0.7),
    'wind_factor': (0.7, 1.3),
    'lw_factor': (0.7, 1.3),
    'ch': (0.0005, 0.002)
}

validate_ranges(current_params, ranges)

5. Summary

After completing these steps, you should have: ✓ Confirmed the configuration file is readable and contains all required parameters
✓ Extracted current calibration parameter values (with validation of scientific notation)
✓ Verified that forcing data and observation data are accessible
✓ Confirmed all current parameter values are within published ranges
✓ Prepared the output directory for simulation results

You are now ready to proceed to glm-calibration to adjust parameters and run the model.