glm-lake-mendota-glm-output（glm 门多塔湖 glm 输出）

GLM Output Guide

Overview

GLM produces NetCDF output containing simulated water temperature profiles. Processing this output requires understanding the coordinate system and matching with observations.

Output File

After running GLM, results are in output/output.nc:

Reading Output with Python

from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

nc = Dataset('output/output.nc', 'r')
time = nc.variables['time'][:]
z = nc.variables['z'][:]
temp = nc.variables['temp'][:]
nc.close()

Coordinate Conversion

Important: GLM z is height from lake bottom, not depth from surface.

# Convert to depth from surface
# Set LAKE_DEPTH based on lake_depth in &init_profiles section of glm3.nml
LAKE_DEPTH = <lake_depth_from_nml>
depth_from_surface = LAKE_DEPTH - z

Complete Output Processing

from netCDF4 import Dataset
import numpy as np
import pandas as pd
from datetime import datetime

def read_glm_output(nc_path, lake_depth):
    nc = Dataset(nc_path, 'r')
    time = nc.variables['time'][:]
    z = nc.variables['z'][:]
    temp = nc.variables['temp'][:]
    start_date = datetime(2009, 1, 1, 12, 0, 0)

    records = []
    for t_idx in range(len(time)):
        hours = float(time[t_idx])
        date = pd.Timestamp(start_date) + pd.Timedelta(hours=hours)
        heights = z[t_idx, :, 0, 0]
        temps = temp[t_idx, :, 0, 0]

        for d_idx in range(len(heights)):
            h_val = heights[d_idx]
            t_val = temps[d_idx]
            if not np.ma.is_masked(h_val) and not np.ma.is_masked(t_val):
                depth = lake_depth - float(h_val)
                if 0 <= depth <= lake_depth:
                    records.append({
                        'datetime': date,
                        'depth': round(depth),
                        'temp_sim': float(t_val)
                    })
    nc.close()

    df = pd.DataFrame(records)
    df = df.groupby(['datetime', 'depth']).agg({'temp_sim': 'mean'}).reset_index()
    return df

Reading Observations

def read_observations(obs_path):
    df = pd.read_csv(obs_path)
    df['datetime'] = pd.to_datetime(df['datetime'])
    df['depth'] = df['depth'].round().astype(int)
    df = df.rename(columns={'temp': 'temp_obs'})
    return df[['datetime', 'depth', 'temp_obs']]

Calculating RMSE

def calculate_rmse(sim_df, obs_df):
    merged = pd.merge(obs_df, sim_df, on=['datetime', 'depth'], how='inner')
    if len(merged) == 0:
        return 999.0
    rmse = np.sqrt(np.mean((merged['temp_sim'] - merged['temp_obs'])**2))
    return rmse

# Usage: get lake_depth from glm3.nml &init_profiles section
sim_df = read_glm_output('output/output.nc', lake_depth=25)
obs_df = read_observations('field_temp_oxy.csv')
rmse = calculate_rmse(sim_df, obs_df)
print(f"RMSE: {rmse:.2f}C")

Common Issues

Best Practices

• Check lake_depth in &init_profiles section of glm3.nml
• Always convert z to depth from surface before comparing with observations
• Round depths to integers for matching
• Group by datetime and depth to handle duplicate records
• Check number of matched observations after merge