netcdf-analysis
Reading and analyzing GLM NetCDF output with Python netCDF4 and pandas for RMSE evaluation.
- License 许可
- MIT
- 最近更新
- 2026-10-02
- Stars
- ★ 92
- Forks
- 7
01 前言 Front Matter
- name
- netcdf-analysis
- description
- Reading and analyzing GLM NetCDF output with Python netCDF4 and pandas for RMSE evaluation.
03 SKILL.md 正文
NetCDF Analysis for GLM Output
Reading GLM Output
import netCDF4 as nc
import numpy as np
import pandas as pd
ds = nc.Dataset('output/output.nc')
temp = ds.variables['temp'][:] # masked array [time, layers]
z = ds.variables['z'][:] # height above bottom [time, layers]
time_var = ds.variables['time']
times = nc.num2date(time_var[:], time_var.units)
Extracting Temperature at Specific Depths
GLM uses variable layer heights. For each timestep:
lake_depth = 25 # from morphometry (crest_elev - min(H))
for t in range(len(times)):
valid = ~temp[t].mask if hasattr(temp[t], 'mask') else np.ones(temp.shape[1], bool)
depths_from_surface = lake_depth - z[t, valid]
temps = temp[t, valid]
# Interpolate to desired depth
RMSE Calculation
# Merge on exact datetime and rounded depth
# rmse = sqrt(mean((obs - sim) ** 2))
Key Notes
- GLM z is height from lake bottom; depth = lake_depth - z
- Round depths to nearest integer for matching
- Use exact datetime matching (no nearest-time)
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