netcdf-metadata

name	netcdf-metadata
description	Extract and analyze metadata from NetCDF files. Use this skill when working with NetCDF (.nc) or CDL (.cdl) files to extract variable information, dimensions, attributes, and data types to CSV format for documentation and analysis.

NetCDF Metadata Extraction

Overview

This skill provides tools for extracting metadata from NetCDF files into structured CSV format. Extract variable names, dimensions, shapes, data types, units, and all NetCDF attributes for documentation, analysis, and understanding NetCDF file contents.

When to Use This Skill

Use this skill when:

• Working with NetCDF (.nc) or CDL (.cdl) files
• Needing to document NetCDF file contents
• Extracting variable lists and attributes to CSV
• Understanding NetCDF file structure before analysis
• Creating metadata catalogs for NetCDF datasets
• Comparing variables across multiple NetCDF files

NetCDF File Formats

Binary NetCDF (.nc files)

Binary format that xarray can read directly. Comes in two versions:

• NetCDF3 (classic): Use engine='scipy' with xarray
• NetCDF4/HDF5: Use engine='h5netcdf' with xarray

CDL Format (.nc.cdl files)

Text representation of NetCDF files. Must be converted to binary using ncgen:

ncgen -o output.nc input.nc.cdl

Required Dependencies

Ensure the project has these dependencies installed:

• xarray - NetCDF file reading
• scipy - Backend for NetCDF3 classic format
• h5netcdf (optional) - Backend for NetCDF4/HDF5 format

Install with:

uv add xarray scipy h5netcdf

Metadata Extraction

Using the Extraction Script

The skill includes scripts/extract_netcdf_metadata.py which extracts all variable metadata to CSV.

Usage:

# Process all .nc files in a directory
uv run python scripts/extract_netcdf_metadata.py

# Process specific files
uv run python scripts/extract_netcdf_metadata.py file1.nc file2.nc

Output: Creates .metadata.csv files alongside each .nc file with the same basename.

CSV Contents:

• variable_name - NetCDF variable identifier
• dimensions - Dimension names (comma-separated)
• shape - Array shape as tuple
• dtype - Data type (float32, int8, etc.)
• ndim - Number of dimensions
• size - Total number of elements
• long_name - Human-readable description (if present)
• units - Measurement units (if present)
• Additional columns for any other NetCDF attributes (flags, FillValue, etc.)

Manual Extraction with xarray

For custom metadata extraction or analysis:

import xarray as xr

# Open NetCDF file (use engine='scipy' for NetCDF3)
ds = xr.open_dataset('file.nc', engine='scipy')

# Access metadata
print(ds)  # Overview of entire dataset
print(ds.dims)  # Dimensions
print(ds.data_vars)  # Data variables

# Access specific variable
var = ds['variable_name']
print(var.dims)  # Variable dimensions
print(var.shape)  # Variable shape
print(var.dtype)  # Data type
print(var.attrs)  # All attributes

# Access specific attributes
if 'long_name' in var.attrs:
    print(var.attrs['long_name'])
if 'units' in var.attrs:
    print(var.attrs['units'])

ds.close()

Converting CDL to Binary NetCDF

When working with .nc.cdl files, convert them first:

import subprocess
from pathlib import Path

cdl_file = Path("input.nc.cdl")
nc_file = cdl_file.with_suffix("").with_suffix(".nc")

subprocess.run(
    ["ncgen", "-o", str(nc_file), str(cdl_file)],
    check=True
)

Then read with xarray as normal.

Common Patterns

Document a Single NetCDF File

# Convert if CDL
ncgen -o data.nc data.nc.cdl

# Extract metadata
uv run python scripts/extract_netcdf_metadata.py data.nc

Result: data.metadata.csv created in the same directory.

Batch Process Multiple Files

# Convert all CDL files in directory
for f in *.nc.cdl; do
    ncgen -o "${f%.cdl}" "$f"
done

# Extract metadata from all
uv run python scripts/extract_netcdf_metadata.py *.nc

Compare Variables Across Files

Extract metadata from multiple files, then compare the CSV files to identify:

• Common variables across datasets
• Different variable names for the same concept
• Missing variables in specific files
• Attribute differences between datasets

Troubleshooting

"file signature not found" error

The NetCDF file is in classic format but xarray is using the wrong backend.

Fix: Use engine='scipy':

ds = xr.open_dataset(file, engine='scipy')

"ncgen not found" error

The ncgen tool is not installed.

Fix: Install NetCDF tools:

# macOS
brew install netcdf

# Ubuntu/Debian
apt install netcdf-bin

Missing backend libraries

xarray requires a backend to read NetCDF files.

Fix: Install scipy for NetCDF3:

uv add scipy

Or h5netcdf for NetCDF4:

uv add h5netcdf

Script Reference

scripts/extract_netcdf_metadata.py

Command-line tool that extracts variable metadata from NetCDF files to CSV format. Run directly without reading into context. The script:

• Accepts one or more NetCDF files as arguments
• Extracts all variable metadata (name, dimensions, shape, dtype, attributes)
• Writes CSV files with .metadata.csv extension alongside the original files
• Handles both NetCDF3 (classic) and NetCDF4 formats automatically
• Organizes CSV columns with standard fields first (variable_name, dimensions, shape, dtype, ndim, size, long_name, units)