skyborn-cdo

Pre-compiled CDO (Climate Data Operators) distributed as a pip-installable Python package.

This is a backend module for Skyborn — an atmospheric science research toolkit.

About CDO 2.6.0

This package bundles CDO 2.6.0 with all required dependencies. For detailed CDO documentation and release information, see:

• CDO Official Project: https://code.mpimet.mpg.de/projects/cdo
• CDO 2.6.0 Release Notes: https://code.mpimet.mpg.de/projects/cdo/news

Installation

pip install skyborn-cdo

This installs a pre-compiled CDO binary along with all required libraries (NetCDF, HDF5, ecCodes, FFTW3, PROJ, UDUNITS2). No system-level CDO installation needed.

Optional dependencies

# xarray support (returnXArray)
pip install skyborn-cdo[xarray]

# Test dependencies
pip install skyborn-cdo[test]

Supported platforms

Platform	Architecture	Status
Linux	x86_64	✅ Supported
macOS	arm64 (Apple Silicon)	✅ Supported
Windows	x86_64	✅ Supported

Quick Start

from skyborn_cdo import Cdo

cdo = Cdo()
print(cdo.version())  # CDO 2.6.0

Usage

skyborn-cdo provides two API styles — command-line style and method-call style — both covering all 900+ CDO operators.

1. Command-line Style — cdo()

Pass a full CDO command string, just like typing in a terminal. The leading cdo prefix is optional.

from skyborn_cdo import Cdo

cdo = Cdo()

# With 'cdo' prefix (copy/paste from terminal)
cdo("cdo -O mergetime in1.nc in2.nc out.nc")

# Without 'cdo' prefix (also valid)
cdo("mergetime in1.nc in2.nc out.nc")

# Complex command with options
cdo("-O -f nc4 sellonlatbox,0,30,0,30 input.nc output.nc")

# Chained operators (CDO pipe syntax)
cdo("-O -f nc4 -fldmean -sellonlatbox,70,140,10,55 input.nc output.nc")
cdo("-O -f nc4 -sp2gpl -setgridtype,regular input.nc output.nc")

2. Method-call Style — cdo.operator()

Each of CDO's 900+ operators is available as a Python method via dynamic dispatch.

from skyborn_cdo import Cdo

cdo = Cdo()

# Basic: operator(parameters, input=..., output=...)
cdo.sellonlatbox("0,30,0,30", input="input.nc", output="output.nc")

# No parameters needed
cdo.copy(input="input.nc", output="output.nc")
cdo.topo(output="topo.nc")

# Multiple input files (space-separated string or list)
cdo.mergetime(input="in1.nc in2.nc in3.nc", output="merged.nc")
cdo.mergetime(input=["in1.nc", "in2.nc", "in3.nc"], output="merged.nc")

# Wildcard / glob patterns (automatically expanded)
cdo.mergetime(input="data_2020*.nc", output="merged.nc")
cdo.mergetime(input="/path/to/data_20200?.nc", output="merged.nc")
cdo.ensmean(input="ensemble_*.nc", output="ensmean.nc")

Wildcards: Both cdo.operator(input="*.nc") and cdo("mergetime *.nc out.nc") support glob patterns (*, ?, [...]). Files are sorted alphabetically before being passed to CDO.

3. Options

CDO options like -O (overwrite), -s (silent), -f nc4 (output format) can be set globally or per-call.

# Global options — applied to every command
cdo = Cdo(options="-O -s")
cdo.copy(input="in.nc", output="out.nc")

# Per-call options — merged with global options
cdo = Cdo()
cdo.copy(input="in.nc", output="out.nc", options="-O -f nc4")

# Common options:
#   -O          Overwrite existing output files
#   -s          Silent mode (suppress informational messages)
#   -f nc4      Output in NetCDF4 format
#   -f nc4c     Output in NetCDF4 classic format
#   -f grb2     Output in GRIB2 format
#   -P 4        Use 4 threads for parallel processing

⚠️ Important: CDO does not overwrite existing output files by default. If the output file already exists, CDO will raise an error: Outputfile out.nc already exists! Use -O to enable overwriting: cdo = Cdo(options="-O") or pass options="-O" per call.

4. Info Operators (return text output)

Operators like info, sinfo, griddes, showname, etc. return their text output as a string instead of writing to a file.

cdo = Cdo()

# File information
info = cdo.sinfo(input="input.nc")
print(info)

# Grid description
grid = cdo.griddes(input="input.nc")
print(grid)

# Show variable names
names = cdo.showname(input="input.nc")
print(names)

# Show timestamps
dates = cdo.showdate(input="input.nc")
print(dates)

# Number of time steps / levels / variables
print(cdo.ntime(input="input.nc"))
print(cdo.nlevel(input="input.nc"))
print(cdo.nvar(input="input.nc"))

5. Return as xarray / netCDF4 / numpy

# Return as xarray.Dataset (requires: pip install skyborn-cdo[xarray])
ds = cdo.sellonlatbox("0,30,0,30", input="input.nc", returnXArray=True)
print(ds)

# Return as netCDF4.Dataset (requires: pip install netCDF4)
nc = cdo.copy(input="input.nc", returnCdf=True)
print(nc.variables.keys())

# Return as numpy array (first variable)
arr = cdo.copy(input="input.nc", returnArray=True)
print(arr.shape)

# Return as masked numpy array
marr = cdo.copy(input="input.nc", returnMaArray=True)

6. Chained Operators (Pipeline)

CDO supports nesting operators in a single command. This is the most powerful feature for building complex processing pipelines.

Command-line style (recommended for complex chains)

cdo = Cdo(options="-O")

# Remapping → Spatial selection → Field mean
cdo("-f nc4 -fldmean -sellonlatbox,70,140,10,55 -remapbil,r180x90 input.nc output.nc")

# Spectral mode conversion pipeline
cdo("-f nc4 -sp2gpl -setgridtype,regular input_spectral.nc output_regular.nc")

# Arithmetic pipeline
cdo("-addc,273.15 -mulc,0.01 input.nc output.nc")

# Conditional masking
cdo("-ifthen -gtc,0 topo.nc topo.nc positive_topo.nc")

Method-call style (using input as sub-command)

cdo = Cdo(options="-O")

# Use CDO operator syntax in the input parameter for chaining
cdo.remapbil("r360x180", input="-mergetime in1.nc in2.nc in3.nc", output="out.nc")
cdo.fldmean(input="-sellonlatbox,70,140,10,55 input.nc", output="mean.nc")
cdo.timmean(input="-sellonlatbox,0,360,-30,30 -remapbil,r180x90 input.nc", output="out.nc")

7. Common Operator Examples

Spatial Operations

cdo = Cdo(options="-O")

# Select region by longitude/latitude box
cdo.sellonlatbox("70,140,10,55", input="global.nc", output="china.nc")

# Select by grid index box
cdo.selindexbox("1,100,1,80", input="input.nc", output="subset.nc")

# Remap to regular grid
cdo.remapbil("r360x180", input="input.nc", output="1deg.nc")     # Bilinear
cdo.remapcon("r360x180", input="input.nc", output="1deg.nc")     # Conservative
cdo.remapnn("r360x180", input="input.nc", output="1deg.nc")      # Nearest neighbor
cdo.remapdis("r360x180", input="input.nc", output="1deg.nc")     # Distance weighted

# Zonal / Meridional mean
cdo.zonmean(input="input.nc", output="zonmean.nc")
cdo.mermean(input="input.nc", output="mermean.nc")

# Invert latitude direction
cdo.invertlat(input="input.nc", output="flipped.nc")

Time Operations

cdo = Cdo(options="-O")

# Time averaging
cdo.timmean(input="input.nc", output="time_avg.nc")
cdo.timstd(input="input.nc", output="time_std.nc")
cdo.timmin(input="input.nc", output="time_min.nc")
cdo.timmax(input="input.nc", output="time_max.nc")

# Monthly / Seasonal / Yearly statistics
cdo.monmean(input="input.nc", output="monthly_mean.nc")
cdo.seasmean(input="input.nc", output="seasonal_mean.nc")
cdo.yearmonmean(input="input.nc", output="yearly_mean.nc")

# Select time steps
cdo.selyear("2020", input="input.nc", output="year2020.nc")
cdo.selmon("1,2,3", input="input.nc", output="jan_mar.nc")
cdo.seltimestep("1/10", input="input.nc", output="first10.nc")

# Set time axis
cdo.settaxis("2020-01-15,12:00,1mon", input="input.nc", output="redate.nc")

# Merge time series
cdo.mergetime(input="jan.nc feb.nc mar.nc", output="q1.nc")

# Split by month / year
cdo.splitmon(input="input.nc", output="monthly_")
cdo.splityear(input="input.nc", output="yearly_")

# Detrend
cdo.detrend(input="input.nc", output="detrended.nc")

Field Statistics

cdo = Cdo(options="-O")

# Field (spatial) statistics
cdo.fldmean(input="input.nc", output="fldmean.nc")
cdo.fldstd(input="input.nc", output="fldstd.nc")
cdo.fldmin(input="input.nc", output="fldmin.nc")
cdo.fldmax(input="input.nc", output="fldmax.nc")
cdo.fldsum(input="input.nc", output="fldsum.nc")

Arithmetic

cdo = Cdo(options="-O")

# Scalar operations
cdo.mulc("2.0", input="input.nc", output="doubled.nc")
cdo.addc("273.15", input="celsius.nc", output="kelvin.nc")
cdo.divc("100", input="input.nc", output="divided.nc")
cdo.subc("273.15", input="kelvin.nc", output="celsius.nc")

# Unary operations
cdo.abs(input="input.nc", output="absolute.nc")
cdo.sqrt(input="positive.nc", output="sqrt.nc")

# Two-file operations
cdo.add(input="a.nc b.nc", output="sum.nc")
cdo.sub(input="a.nc b.nc", output="diff.nc")
cdo.mul(input="a.nc b.nc", output="product.nc")
cdo.div(input="a.nc b.nc", output="ratio.nc")

# Custom expression
cdo.expr("'new_var=temp*2+precip;'", input="input.nc", output="computed.nc")

Vertical Level Operations

cdo = Cdo(options="-O")

# Select specific levels
cdo.sellevel("85000,50000,20000", input="input.nc", output="levels.nc")

# Interpolate to new levels
cdo.intlevel("90000,85000,70000,50000,30000", input="input.nc", output="interp.nc")

# Show levels
print(cdo.showlevel(input="input.nc"))

Grid Type Conversion & Spectral Transforms

cdo = Cdo(options="-O")

# Convert grid type
cdo.setgridtype("regular", input="input.nc", output="regular.nc")

# Spectral ↔ Grid-point transforms
cdo.sp2gpl(input="spectral.nc", output="gaussian_linear.nc")
cdo.sp2gp(input="spectral.nc", output="gaussian.nc")
cdo.gp2sp(input="gaussian.nc", output="spectral.nc")

# Complex chain: spectral to regular grid in NetCDF4
cdo("-O -f nc4 -sp2gpl -setgridtype,regular spectral.nc regular.nc")

Format Conversion

cdo = Cdo(options="-O")

# NetCDF formats
cdo.copy(input="input.nc", output="out.nc", options="-f nc4")     # NetCDF4
cdo.copy(input="input.nc", output="out.nc", options="-f nc4c")    # NetCDF4 Classic
cdo.copy(input="input.nc", output="out.nc", options="-f nc2")     # NetCDF 64-bit

# GRIB formats
cdo.copy(input="input.nc", output="out.grb", options="-f grb")    # GRIB1
cdo.copy(input="input.nc", output="out.grb2", options="-f grb2")  # GRIB2

Copy vs Clone — Understanding File Size Changes

When using copy operator, output file may be significantly larger than input file. This is expected behavior and not a bug.

Why does copy increase file size?

The copy operator:

1. Reads and decompresses all data from input file into memory
2. Writes uncompressed to output file (by default CDO does not apply compression)

Example: A compressed NetCDF4 file with deflate compression may be 28 MB. After cdo.copy(), the output becomes 47 MB (uncompressed original data).

Solutions:

Use clone for file duplication (no re-compression):

cdo = Cdo(options="-O")

# Fastest and smallest: clone preserves compression, no decode/encode
cdo.clone(input="compressed_file.nc", output="output.nc")

Use -z zip option to re-compress with deflate:

cdo = Cdo(options="-O")

# Force deflate compression (level 1, matches typical NetCDF4 default)
cdo.copy(input="input.nc", output="output.nc", options="-z zip")

# Specify compression level (1=low/fast, 9=high/slow)
cdo.copy(input="input.nc", output="output.nc", options="-z zip_5")

# Both command styles work
cdo("-O -z zip copy input.nc output.nc")

Difference between copy vs clone:

Operator	Compression	Speed	Use Case	Output Size
copy	Default (none)	Decodes all data	Data inspection, reformat	Larger
copy -z zip	Deflate (level 1)	Slower (re-compress)	Archive, storage	Smaller
clone	Preserves original	Fast (no decode)	Direct duplication	Same as input

cdo = Cdo(options="-O")

# Input file: data.nc (28.2 MB, compressed with deflate)

# ❌ Don't do this (output: 47.5 MB)
cdo.copy(input="data.nc", output="output.nc")

# ✅ Do this instead - with compression
cdo.copy(input="data.nc", output="output.nc", options="-z zip")  # output: ~28 MB

# ✅ Or use clone for fastest/smallest direct copy
cdo.clone(input="data.nc", output="output.nc")  # output: ~28 MB (identical)

Variable Metadata

cdo = Cdo(options="-O")

# Rename variable
cdo.chname("old_name,new_name", input="input.nc", output="renamed.nc")

# Set attributes
cdo.setattribute("varname@units=kg/m2", input="input.nc", output="units.nc")

# Set missing value
cdo.setmissval("-999", input="input.nc", output="newmiss.nc")

Ensemble Operations

cdo = Cdo(options="-O")

# Ensemble mean (multiple realizations)
cdo.ensmean(input="run_*.nc", output="ensemble_mean.nc")

# Ensemble standard deviation
cdo.ensstd(input="run_*.nc", output="ensemble_std.nc")

# Ensemble variance
cdo.ensvar(input="run_001.nc run_002.nc run_003.nc", output="ensemble_var.nc")

# Ensemble sum
cdo.enssum(input=["member_1.nc", "member_2.nc", "member_3.nc"], output="ens_sum.nc")

# Ensemble percentiles
cdo.enspctl("25,50,75", input="run_*.nc", output="ensemble_quartiles.nc")

Grid Information and Modification

cdo = Cdo(options="-O")

# Calculate grid cell areas
cdo.gridarea(input="input.nc", output="grid_areas.nc")

# Calculate grid weights (for weighted averaging)
cdo.gridweights(input="input.nc", output="weights.nc")

# Set grid type
cdo.setgridtype("regular", input="curvilinear.nc", output="regular.nc")

# Show grid description
print(cdo.griddes(input="input.nc"))

# Invert latitude direction (flip N-S)
cdo.invertlat(input="input.nc", output="flipped.nc")

Advanced Statistical Operations

cdo = Cdo(options="-O")

# Field percentiles
cdo.fldpctl("10,50,90", input="input.nc", output="field_percentiles.nc")

# Field range (max - min)
cdo.fldrange(input="input.nc", output="field_range.nc")

# Time series percentiles
cdo.timpctl("25,50,75", input="timeseries.nc", output="time_percentiles.nc")

# Running mean (e.g., 5-timestep window)
cdo.runmean("5", input="input.nc", output="smoothed.nc")

# Trend removal
cdo.detrend(input="input.nc", output="detrended.nc")

8. Error Handling

skyborn-cdo captures all CDO errors and raises them as CdoError exceptions with full diagnostic information.

from skyborn_cdo import Cdo, CdoError

cdo = Cdo()

try:
    cdo.sellonlatbox("0,30,0,30", input="nonexistent.nc", output="out.nc")
except CdoError as e:
    print(f"Error: {e}")
    print(f"Return code: {e.returncode}")
    print(f"CDO stderr: {e.stderr}")
    print(f"Command: {e.cmd}")

Common errors:

• File not found: Open failed on >file.nc< No such file or directory
• Output exists: Outputfile out.nc already exists! → Add -O option
• Invalid parameters: Float parameter >abc< contains invalid character
• Timeout: CDO command timed out after 60s → Increase timeout

9. Timeout

# Global timeout for all commands (seconds)
cdo = Cdo(timeout=300)

# Per-call timeout override
cdo.remapcon("r3600x1800", input="input.nc", output="hires.nc", timeout=600)
cdo("cdo -O remapcon,r3600x1800 input.nc hires.nc", timeout=600)

10. Debug Mode

# Print executed commands and CDO stderr output
cdo = Cdo(debug=True)
cdo.sellonlatbox("0,30,0,30", input="input.nc", output="output.nc")
# [skyborn-cdo] Running: /path/to/cdo -sellonlatbox,0,30,0,30 input.nc output.nc
# [skyborn-cdo] stderr: cdo    sellonlatbox: ...

11. Getting Help

Python API — cdo.help()

from skyborn_cdo import Cdo

cdo = Cdo()

# Help for a specific operator
print(cdo.help("sellonlatbox"))
print(cdo.help("mergetime"))
print(cdo.help("remapbil"))

# General usage summary
print(cdo.help())

# List all available operators
print(cdo.operators())

# Check if an operator exists
cdo.has_operator("sellonlatbox")  # True

CLI — skyborn-cdo -h / --help

# Show general help
skyborn-cdo --help

# Show help for a specific CDO operator (passed to CDO directly)
skyborn-cdo -h sellonlatbox
skyborn-cdo -h mergetime
skyborn-cdo -h remapbil

# List all operators
skyborn-cdo --operators

# Also works via python -m
python -m skyborn_cdo --help
python -m skyborn_cdo -h sellonlatbox

CLI

The package provides a skyborn-cdo command-line tool (also available as python -m skyborn_cdo):

# Show installation info and CDO version
skyborn-cdo --info

# Show help
skyborn-cdo --help

# Operator-level help (forwarded to CDO)
skyborn-cdo -h sellonlatbox

# List all operators
skyborn-cdo --operators

# Pass-through to CDO (any CDO command works)
skyborn-cdo -O -f nc4 copy input.nc output.nc
skyborn-cdo mergetime in1.nc in2.nc out.nc
skyborn-cdo -O -f nc4 -sp2gpl -setgridtype,regular spectral.nc regular.nc

CDO Operators

skyborn-cdo provides access to 938 CDO operators covering all aspects of climate data processing. Check operator availability:

cdo = Cdo()
print(len(cdo.operators()))  # 938
cdo.has_operator("sellonlatbox")  # True
print(cdo.help("sellonlatbox"))  # Get operator documentation

Operator Categories

Category	Count	Examples	Use Cases
Time Operations	208	timmean, timstd, mergetime, seldate, monmean, yearsum	Time series analysis, temporal statistics, date/time selection
Statistical	222	fldmean, fldstd, ensmean, timavg, zonmean, mermean	Spatial/temporal statistics, ensemble analysis
Spatial Selection	35	sellonlatbox, selindexbox, selgrid, sellevel, selcode	Regional extraction, level selection
Grid/Remapping	64	remapbil, remapcon, remapnn, setgrid, gridarea	Grid conversion, interpolation, regridding
Arithmetic	60	add, mul, expr, addc, sqrt, log	Mathematical operations, calculations
Vertical Levels	13	ml2pl, intlevel, pressure, sealevelpressure	Model level conversion, vertical interpolation
Format/IO	47	copy, merge, split, cat, import_*, output*	File operations, format conversion
Info/Query	50	info, showname, griddes, ntime, showdate	File inspection, metadata extraction
Spectral/Grid Transform	28	sp2gp, gp2sp, sp2gpl, fourier	Spectral transforms, Fourier analysis
Others	211	Specialized operators for specific domains	Advanced climate computations

Total: 938 operators

Are All Operators Useful?

Commonly used (daily work): ~80-100 operators

• Space: sellonlatbox, remapbil, remapcon, zonmean, mermean
• Time: mergetime, timmean, monmean, yearsum, seldate, selyear
• Statistics: fldmean, fldstd, fldmin, fldmax, ensmean
• Arithmetic: add, sub, mul, div, addc, mulc, expr
• Info: sinfo, showname, griddes, ntime

Specialized operators (400+): Domain-specific

• Meteorology: sealevelpressure, ml2pl, geopotheight
• Oceanography: detrend, dmean, seasmean
• Climate indices: eca_* (extreme climate events), ydrun* (running means)
• Statistical analysis: trend, regres, corr, eof
• Spectral analysis: sp2gp, gp2sp, dft, filter

Legacy/Niche operators (400+): Less frequently used but available

• Format-specific imports (import_cmsaf, import_grads)
• Experimental features (remapavgtest, remapcon2test)
• Highly specialized computations

How to decide if you need an operator:

# Search operators by keyword
cdo = Cdo()
time_ops = [op for op in cdo.operators() if 'time' in op]
print(f"Time-related: {len(time_ops)} operators")  # 208

# Get detailed help for any operator
print(cdo.help("operator_name"))

CDO Version

This package bundles CDO 2.6.0 with the following libraries:

• NetCDF-C 4.9.x
• HDF5 1.14.x
• ecCodes 2.40+
• FFTW3 3.3.10
• PROJ 9.5.x
• UDUNITS2 2.2.28

Exact library versions vary by platform (Linux/macOS build from source, Windows uses MSYS2 packages).

What's New in CDO 2.6.0

New operators

Operator	Description
varsskew	Ensemble skewness across input files (VarsStat group)
varskurt	Ensemble kurtosis across input files (VarsStat group)
varsmedian	Ensemble median across input files (VarsStat group)
varspctl	Ensemble percentile across input files, e.g. varspctl,90
symmetrize	Mirrors data at the equator (creates symmetric fields)
splitensemble	Splits GRIB2 ensemble members into separate files

New global options

Option	Description
--query	Pre-selects a subset of the data cube from a dataset
--async_read true|false	Reads input data asynchronously; available for diff, info, trend, detrend, Timstat operators

Performance improvements

• Significant performance improvement for reading HEALPix zarr datasets with NCZARR.

Bug fixes

• fillmiss: wrong result when fewer than 4 valid neighbours available [Bug #12341]
• chparam: failed since release 2.5.3 [Bug #12328]

Usage examples

cdo = Cdo()

# VarsStat: compute skewness / kurtosis / median across an ensemble
cdo.varsskew(input=["mem1.nc", "mem2.nc", "mem3.nc"], output="skew.nc")
cdo.varskurt(input=["mem1.nc", "mem2.nc", "mem3.nc"], output="kurt.nc")
cdo.varsmedian(input=["mem1.nc", "mem2.nc", "mem3.nc"], output="median.nc")
cdo.varspctl("90", input=["mem1.nc", "mem2.nc", "mem3.nc"], output="pct90.nc")

# Symmetrize: mirror a field at the equator
cdo.symmetrize(input="topo.nc", output="topo_symmetric.nc")

# Global async read option (speeds up supported operators)
cdo("--async_read true -diff file1.nc file2.nc")

Operator Reference

Complete listing of all operators available in CDO 2.6.0 (bundled with skyborn-cdo), grouped by category. Use cdo.help("operator_name") for detailed usage.

Information

Operator	Description
info	Dataset information listed by identifier
infon	Dataset information listed by name
cinfo	Compact information listed by name
map	Dataset information and simple map
sinfo	Short information listed by identifier
sinfon	Short information listed by name
xsinfo	Extra short information listed by name
xsinfop	Extra short information listed by identifier
diff	Compare two datasets listed by identifier
diffn	Compare two datasets listed by name
npar	Number of parameters
nlevel	Number of levels
nyear	Number of years
nmon	Number of months
ndate	Number of dates
ntime	Number of timesteps
ngridpoints	Number of gridpoints
ngrids	Number of horizontal grids
showformat	Show file format
showcode	Show code numbers
showname	Show variable names
showstdname	Show standard names
showlevel	Show levels
showltype	Show GRIB level types
showyear	Show years
showmon	Show months
showdate	Show date information
showtime	Show time information
showtimestamp	Show timestamp
showchunkspec	Show chunk specification
showfilter	Show filter specification
showattribute	Show attributes
partab	Parameter table
codetab	Parameter code table
griddes	Grid description
zaxisdes	Z-axis description
vct	Vertical coordinate table

File Operations

Operator	Description
copy	Copy datasets
clone	Clone datasets
cat	Concatenate datasets
tee	Duplicate a data stream and write it to file
pack	Pack data
unpack	Unpack data
setchunkspec	Specify chunking
setfilter	Specify filter
bitrounding	Bit rounding
replace	Replace variables
duplicate	Duplicates a dataset
mergegrid	Merge grid
merge	Merge datasets with different fields
mergetime	Merge datasets sorted by date and time
splitcode	Split code numbers
splitparam	Split parameter identifiers
splitname	Split variable names
splitlevel	Split levels
splitgrid	Split grids
splitzaxis	Split z-axes
splittabnum	Split parameter table numbers
splithour	Split hours
splitday	Split days
splitseas	Split seasons
splityear	Split years
splityearmon	Split in years and months
splitmon	Split months
splitsel	Split selected timesteps
splitdate	Splits a file into dates
distgrid	Distribute horizontal grid
collgrid	Collect horizontal grid

Selection

Operator	Description
select	Select fields
delete	Delete fields
selmulti	Select multiple fields
delmulti	Delete multiple fields
changemulti	Change identification of multiple fields
selparam	Select parameters by identifier
delparam	Delete parameters by identifier
selcode	Select parameters by code number
delcode	Delete parameters by code number
selname	Select parameters by name
delname	Delete parameters by name
selstdname	Select parameters by standard name
sellevel	Select levels
sellevidx	Select levels by index
selgrid	Select grids
selzaxis	Select z-axes
selzaxisname	Select z-axes by name
selltype	Select GRIB level types
seltabnum	Select parameter table numbers
seltimestep	Select timesteps
seltime	Select times
selhour	Select hours
selday	Select days
selmonth	Select months (alias: selmon)
selyear	Select years
selseason	Select seasons
seldate	Select dates
selsmon	Select single month
sellonlatbox	Select a longitude/latitude box
selindexbox	Select an index box
selregion	Select cells inside regions
selcircle	Select cells inside a circle
selgridcell	Select grid cells
delgridcell	Delete grid cells
samplegrid	Resample grid cells
selyearidx	Select year by index
seltimeidx	Select timestep by index
bottomvalue	Extract bottom level
topvalue	Extract top level
isosurface	Extract isosurface

Conditional

Operator	Description
ifthen	If then
ifnotthen	If not then
ifthenelse	Conditional selection
ifthenc	If then constant
ifnotthenc	If not then constant
reducegrid	Reduce fields to user-defined mask

Comparison

Operator	Description
eq	Equal
ne	Not equal
le	Less equal
lt	Less than
ge	Greater equal
gt	Greater than
eqc	Equal constant
nec	Not equal constant
lec	Less equal constant
ltc	Less than constant
gec	Greater equal constant
gtc	Greater than constant
ymoneq	Compare time series with Equal (multi-year monthly)
ymonne	Compare time series with NotEqual (multi-year monthly)
ymonle	Compare time series with LessEqual (multi-year monthly)
ymonlt	Compare time series with LessThan (multi-year monthly)
ymonge	Compare time series with GreaterEqual (multi-year monthly)
ymongt	Compare time series with GreaterThan (multi-year monthly)
yseaseq	Compare time series with Equal (multi-year seasonal)
yseasne	Compare time series with NotEqual (multi-year seasonal)
yseasle	Compare time series with LessEqual (multi-year seasonal)
yseaslt	Compare time series with LessThan (multi-year seasonal)
yseasge	Compare time series with GreaterEqual (multi-year seasonal)
yseasgt	Compare time series with GreaterThan (multi-year seasonal)

Modification

Operator	Description
setattribute	Set attributes
delattribute	Delete attributes
setpartabp	Set parameter table (by path)
setpartabn	Set parameter table (by name)
setcodetab	Set parameter code table
setcode	Set code number
setparam	Set parameter identifier
setname	Set variable name
setstdname	Set standard name
setunit	Set variable unit
setlevel	Set level
setltype	Set GRIB level type
setmaxsteps	Set max timesteps
setdate	Set date
settime	Set time of the day
setday	Set day
setmon	Set month
setyear	Set year
settunits	Set time units
settaxis	Set time axis
settbounds	Set time bounds
setreftime	Set reference time
setcalendar	Set calendar
shifttime	Shift timesteps
chcode	Change code number
chparam	Change parameter identifier
chname	Change variable or coordinate name
chunit	Change variable unit
chlevel	Change level
chlevelc	Change level of one code
chlevelv	Change level of one variable
setgrid	Set grid
setgridtype	Set grid type
setgridarea	Set grid cell area
setgridmask	Set grid mask
setprojparams	Set proj params
setzaxis	Set z-axis
genlevelbounds	Generate level bounds
invertlat	Invert latitudes
invertlev	Invert levels
shiftx	Shift x
shifty	Shift y
maskregion	Mask regions
masklonlatbox	Mask a longitude/latitude box
maskindexbox	Mask an index box
setclonlatbox	Set a longitude/latitude box to constant
setcindexbox	Set an index box to constant
enlarge	Enlarge fields
setmissval	Set a new missing value
setctomiss	Set constant to missing value
setmisstoc	Set missing value to constant
setrtomiss	Set range to missing value
setvrange	Set valid range
setmisstonn	Set missing value to nearest neighbor
setmisstodis	Set missing value to distance-weighted average
vertfillmiss	Vertical filling of missing values
timfillmiss	Temporal filling of missing values
setgridcell	Set the value of a grid cell

Arithmetic

Operator	Description
expr	Evaluate expressions
exprf	Evaluate expressions script
aexpr	Evaluate expressions and append results
aexprf	Evaluate expression script and append results
abs	Absolute value
int	Integer value
nint	Nearest integer value
pow	Power
sqr	Square
sqrt	Square root
exp	Exponential
ln	Natural logarithm
log10	Base 10 logarithm
sin	Sine
cos	Cosine
tan	Tangent
asin	Arc sine
acos	Arc cosine
atan	Arc tangent
reci	Reciprocal value
not	Logical NOT
addc	Add a constant
subc	Subtract a constant
mulc	Multiply with a constant
divc	Divide by a constant
minc	Minimum of a field and a constant
maxc	Maximum of a field and a constant
add	Add two fields
sub	Subtract two fields
mul	Multiply two fields
div	Divide two fields
min	Minimum of two fields
max	Maximum of two fields
atan2	Arc tangent of two fields
setmiss	Set missing values
dayadd	Add daily time series
daysub	Subtract daily time series
daymul	Multiply daily time series
daydiv	Divide daily time series
monadd	Add monthly time series
monsub	Subtract monthly time series
monmul	Multiply monthly time series
mondiv	Divide monthly time series
yearadd	Add yearly time series
yearsub	Subtract yearly time series
yearmul	Multiply yearly time series
yeardiv	Divide yearly time series
yhouradd	Add multi-year hourly time series
yhoursub	Subtract multi-year hourly time series
yhourmul	Multiply multi-year hourly time series
yhourdiv	Divide multi-year hourly time series
ydayadd	Add multi-year daily time series
ydaysub	Subtract multi-year daily time series
ydaymul	Multiply multi-year daily time series
ydaydiv	Divide multi-year daily time series
ymonadd	Add multi-year monthly time series
ymonsub	Subtract multi-year monthly time series
ymonmul	Multiply multi-year monthly time series
ymondiv	Divide multi-year monthly time series
yseasadd	Add multi-year seasonal time series
yseassub	Subtract multi-year seasonal time series
yseasmul	Multiply multi-year seasonal time series
yseasdiv	Divide multi-year seasonal time series
muldpm	Multiply with days per month
divdpm	Divide by days per month
muldpy	Multiply with days per year
divdpy	Divide by days per year
mulcoslat	Multiply with the cosine of the latitude
divcoslat	Divide by cosine of the latitude

Statistics

Cumulative / Consecutive

Operator	Description
timcumsum	Cumulative sum over all timesteps
consecsum	Consecutive sum
consects	Consecutive timesteps

Variables Statistics (across multiple input files)

Operator	Description
varsmin	Variables minimum
varsmax	Variables maximum
varsrange	Variables range
varssum	Variables sum
varsmean	Variables mean
varsavg	Variables average
varsstd	Variables standard deviation
varsstd1	Variables standard deviation (n-1)
varsvar	Variables variance
varsvar1	Variables variance (n-1)
varsskew	Variables skewness
varskurt	Variables kurtosis
varsmedian	Variables median
varspctl	Variables percentile

Ensemble Statistics

Operator	Description
ensmin	Ensemble minimum
ensmax	Ensemble maximum
ensrange	Ensemble range
enssum	Ensemble sum
ensmean	Ensemble mean
ensavg	Ensemble average
ensstd	Ensemble standard deviation
ensstd1	Ensemble standard deviation (n-1)
ensvar	Ensemble variance
ensvar1	Ensemble variance (n-1)
ensskew	Ensemble skewness
enskurt	Ensemble kurtosis
ensmedian	Ensemble median
enspctl	Ensemble percentile
ensrkhistspace	Ranked Histogram averaged over space
ensrkhisttime	Ranked Histogram averaged over time
ensroc	Ensemble Receiver Operating characteristics
enscrps	Ensemble CRPS and decomposition
ensbrs	Ensemble Brier score

Field Statistics (spatial reduction to scalar per timestep)

Operator	Description
fldmin	Field minimum
fldmax	Field maximum
fldrange	Field range
fldsum	Field sum
fldint	Field integral
fldmean	Field mean
fldavg	Field average
fldstd	Field standard deviation
fldstd1	Field standard deviation (n-1)
fldvar	Field variance
fldvar1	Field variance (n-1)
fldskew	Field skewness
fldkurt	Field kurtosis
fldmedian	Field median
fldcount	Field count
fldpctl	Field percentile

Zonal Statistics (average along longitude axis)

Operator	Description
zonmin	Zonal minimum
zonmax	Zonal maximum
zonrange	Zonal range
zonsum	Zonal sum
zonmean	Zonal mean
zonavg	Zonal average
zonstd	Zonal standard deviation
zonstd1	Zonal standard deviation (n-1)
zonvar	Zonal variance
zonvar1	Zonal variance (n-1)
zonskew	Zonal skewness
zonkurt	Zonal kurtosis
zonmedian	Zonal median
zonpctl	Zonal percentile

Meridional Statistics (average along latitude axis)

Operator	Description
mermin	Meridional minimum
mermax	Meridional maximum
merrange	Meridional range
mersum	Meridional sum
mermean	Meridional mean
meravg	Meridional average
merstd	Meridional standard deviation
merstd1	Meridional standard deviation (n-1)
mervar	Meridional variance
mervar1	Meridional variance (n-1)
merskew	Meridional skewness
merkurt	Meridional kurtosis
mermedian	Meridional median
merpctl	Meridional percentile

Gridbox Statistics (aggregate rectangular blocks of grid cells)

Operator	Description
gridboxmin	Gridbox minimum
gridboxmax	Gridbox maximum
gridboxrange	Gridbox range
gridboxsum	Gridbox sum
gridboxmean	Gridbox mean
gridboxavg	Gridbox average
gridboxstd	Gridbox standard deviation
gridboxstd1	Gridbox standard deviation (n-1)
gridboxvar	Gridbox variance
gridboxvar1	Gridbox variance (n-1)
gridboxskew	Gridbox skewness
gridboxkurt	Gridbox kurtosis
gridboxmedian	Gridbox median

Remap Statistics

Operator	Description
remapmin	Remap minimum
remapmax	Remap maximum
remaprange	Remap range
remapsum	Remap sum
remapmean	Remap mean
remapavg	Remap average
remapstd	Remap standard deviation
remapstd1	Remap standard deviation (n-1)
remapvar	Remap variance
remapvar1	Remap variance (n-1)
remapskew	Remap skewness
remapkurt	Remap kurtosis
remapmedian	Remap median

Vertical Statistics

Operator	Description
vertmin	Vertical minimum
vertmax	Vertical maximum
vertrange	Vertical range
vertsum	Vertical sum
vertmean	Vertical mean
vertavg	Vertical average
vertstd	Vertical standard deviation
vertstd1	Vertical standard deviation (n-1)
vertvar	Vertical variance
vertvar1	Vertical variance (n-1)

Time Selection Statistics

Operator	Description
timselmin	Time selection minimum
timselmax	Time selection maximum
timselrange	Time selection range
timselsum	Time selection sum
timselmean	Time selection mean
timselavg	Time selection average
timselstd	Time selection standard deviation
timselstd1	Time selection standard deviation (n-1)
timselvar	Time selection variance
timselvar1	Time selection variance (n-1)
timselpctl	Time range percentile

Running Statistics

Operator	Description
runmin	Running minimum
runmax	Running maximum
runrange	Running range
runsum	Running sum
runmean	Running mean
runavg	Running average
runstd	Running standard deviation
runstd1	Running standard deviation (n-1)
runvar	Running variance
runvar1	Running variance (n-1)
runpctl	Running percentile

Time Statistics (reduce all timesteps to one)

Operator	Description
timmin	Time minimum
timmax	Time maximum
timminidx	Index of time minimum
timmaxidx	Index of time maximum
timrange	Time range
timsum	Time sum
timmean	Time mean
timavg	Time average
timstd	Time standard deviation
timstd1	Time standard deviation (n-1)
timvar	Time variance
timvar1	Time variance (n-1)
timpctl	Temporal percentile

Hourly Statistics

Operator	Description
hourmin	Hourly minimum
hourmax	Hourly maximum
hourrange	Hourly range
hoursum	Hourly sum
hourmean	Hourly mean
houravg	Hourly average
hourstd	Hourly standard deviation
hourstd1	Hourly standard deviation (n-1)
hourvar	Hourly variance
hourvar1	Hourly variance (n-1)
hourpctl	Hourly percentile

Daily Statistics

Operator	Description
daymin	Daily minimum
daymax	Daily maximum
dayrange	Daily range
daysum	Daily sum
daymean	Daily mean
dayavg	Daily average
daystd	Daily standard deviation
daystd1	Daily standard deviation (n-1)
dayvar	Daily variance
dayvar1	Daily variance (n-1)
daypctl	Daily percentile

Monthly Statistics

Operator	Description
monmin	Monthly minimum
monmax	Monthly maximum
monrange	Monthly range
monsum	Monthly sum
monmean	Monthly mean
monavg	Monthly average
monstd	Monthly standard deviation
monstd1	Monthly standard deviation (n-1)
monvar	Monthly variance
monvar1	Monthly variance (n-1)
monpctl	Monthly percentile

Yearly Statistics

Operator	Description
yearmonmean	Yearly mean from monthly data
yearmin	Yearly minimum
yearmax	Yearly maximum
yearminidx	Index of yearly minimum
yearmaxidx	Index of yearly maximum
yearrange	Yearly range
yearsum	Yearly sum
yearmean	Yearly mean
yearavg	Yearly average
yearstd	Yearly standard deviation
yearstd1	Yearly standard deviation (n-1)
yearvar	Yearly variance
yearvar1	Yearly variance (n-1)
yearpctl	Yearly percentile

Seasonal Statistics

Operator	Description
seasmin	Seasonal minimum
seasmax	Seasonal maximum
seasrange	Seasonal range
seassum	Seasonal sum
seasmean	Seasonal mean
seasavg	Seasonal average
seasstd	Seasonal standard deviation
seasstd1	Seasonal standard deviation (n-1)
seasvar	Seasonal variance
seasvar1	Seasonal variance (n-1)
seaspctl	Seasonal percentile

Multi-year Hourly Statistics

Operator	Description
yhourmin	Multi-year hourly minimum
yhourmax	Multi-year hourly maximum
yhourrange	Multi-year hourly range
yhoursum	Multi-year hourly sum
yhourmean	Multi-year hourly mean
yhouravg	Multi-year hourly average
yhourstd	Multi-year hourly standard deviation
yhourstd1	Multi-year hourly standard deviation (n-1)
yhourvar	Multi-year hourly variance
yhourvar1	Multi-year hourly variance (n-1)

Multi-day Hourly Statistics

Operator	Description
dhourmin	Multi-day hourly minimum
dhourmax	Multi-day hourly maximum
dhourrange	Multi-day hourly range
dhoursum	Multi-day hourly sum
dhourmean	Multi-day hourly mean
dhouravg	Multi-day hourly average
dhourstd	Multi-day hourly standard deviation
dhourstd1	Multi-day hourly standard deviation (n-1)
dhourvar	Multi-day hourly variance
dhourvar1	Multi-day hourly variance (n-1)
dminutemin	Multi-day by the minute minimum
dminutemax	Multi-day by the minute maximum
dminuterange	Multi-day by the minute range
dminutesum	Multi-day by the minute sum
dminutemean	Multi-day by the minute mean
dminuteavg	Multi-day by the minute average
dminutestd	Multi-day by the minute standard deviation
dminutestd1	Multi-day by the minute standard deviation (n-1)
dminutevar	Multi-day by the minute variance
dminutevar1	Multi-day by the minute variance (n-1)

Multi-year Daily Statistics

Operator	Description
ydaymin	Multi-year daily minimum
ydaymax	Multi-year daily maximum
ydayrange	Multi-year daily range
ydaysum	Multi-year daily sum
ydaymean	Multi-year daily mean
ydayavg	Multi-year daily average
ydaystd	Multi-year daily standard deviation
ydaystd1	Multi-year daily standard deviation (n-1)
ydayvar	Multi-year daily variance
ydayvar1	Multi-year daily variance (n-1)
ydaypctl	Multi-year daily percentile

Multi-year Monthly Statistics

Operator	Description
ymonmin	Multi-year monthly minimum
ymonmax	Multi-year monthly maximum
ymonrange	Multi-year monthly range
ymonsum	Multi-year monthly sum
ymonmean	Multi-year monthly mean
ymonavg	Multi-year monthly average
ymonstd	Multi-year monthly standard deviation
ymonstd1	Multi-year monthly standard deviation (n-1)
ymonvar	Multi-year monthly variance
ymonvar1	Multi-year monthly variance (n-1)
ymonpctl	Multi-year monthly percentile

Multi-year Seasonal Statistics

Operator	Description
yseasmin	Multi-year seasonal minimum
yseasmax	Multi-year seasonal maximum
yseasrange	Multi-year seasonal range
yseassum	Multi-year seasonal sum
yseasmean	Multi-year seasonal mean
yseasavg	Multi-year seasonal average
yseasstd	Multi-year seasonal standard deviation
yseasstd1	Multi-year seasonal standard deviation (n-1)
yseasvar	Multi-year seasonal variance
yseasvar1	Multi-year seasonal variance (n-1)
yseaspctl	Multi-year seasonal percentile

Multi-year Daily Running Statistics

Operator	Description
ydrunmin	Multi-year daily running minimum
ydrunmax	Multi-year daily running maximum
ydrunsum	Multi-year daily running sum
ydrunmean	Multi-year daily running mean
ydrunavg	Multi-year daily running average
ydrunstd	Multi-year daily running standard deviation
ydrunstd1	Multi-year daily running standard deviation (n-1)
ydrunvar	Multi-year daily running variance
ydrunvar1	Multi-year daily running variance (n-1)
ydrunpctl	Multi-year daily running percentile

Correlation and Regression

Operator	Description
fldcor	Correlation in grid space
timcor	Correlation over time
fldcovar	Covariance in grid space
timcovar	Covariance over time
regres	Regression
detrend	Detrend time series
trend	Trend of time series
addtrend	Add trend
subtrend	Subtract trend

EOFs

Operator	Description
eof	Calculate EOFs in spatial or time space
eoftime	Calculate EOFs in time space
eofspatial	Calculate EOFs in spatial space
eof3d	Calculate 3-Dimensional EOFs in time space
eofcoeff	Principal coefficients of EOFs

Interpolation

Operator	Description
remapbil	Bilinear interpolation
genbil	Generate bilinear interpolation weights
remapbic	Bicubic interpolation
genbic	Generate bicubic interpolation weights
remapknn	k-nearest neighbor remapping
remapnn	Nearest neighbor remapping
remapdis	Distance weighted average remapping
genknn	Generate k-nearest neighbor remap weights
gennn	Generate nearest neighbor remap weights
gendis	Generate distance weighted average remap weights
remapcon	First order conservative remapping
gencon	Generate 1st order conservative remap weights
remaplaf	Largest area fraction remapping
genlaf	Generate largest area fraction remap weights
remap	Grid remapping (with pre-computed weights)
remapeta	Remap vertical hybrid levels
ml2pl	Model to pressure level interpolation
ap2pl	Vertical pressure interpolation
gh2hl	Geometric height interpolation
intlevel	Linear level interpolation
intlevel3d	Linear level interpolation from/to 3D vertical coordinates
inttime	Interpolation between timesteps
intntime	Interpolation between n timesteps
intyear	Interpolation between two years

Transformation

Operator	Description
sp2gp	Spectral to gridpoint
gp2sp	Gridpoint to spectral
sp2sp	Spectral to spectral (truncation change)
sp2gpl	Spectral to Gaussian gridpoint (linear)
dv2ps	Divergence and vorticity to velocity potential and stream function
dv2uv	Divergence and vorticity to U and V wind
uv2dv	U and V wind to divergence and vorticity
fourier	Fourier transformation

Import / Export

Operator	Description
import_binary	Import binary data sets
import_cmsaf	Import CM-SAF HDF5 files
input	ASCII input
inputsrv	SERVICE ASCII input
inputext	EXTRA ASCII input
output	ASCII output
outputf	Formatted output
outputint	Integer output
outputsrv	SERVICE ASCII output
outputext	EXTRA ASCII output
outputtab	Table output
gmtxyz	GMT xyz format
gmtcells	GMT multiple segment format

Miscellaneous

Operator	Description
gradsdes	GrADS data descriptor file
after	ECHAM standard post processor
bandpass	Bandpass filtering
lowpass	Lowpass filtering
highpass	Highpass filtering
gridarea	Grid cell area
gridweights	Grid cell weights
smooth	Smooth grid points
smooth9	9 point smoothing
deltat	Difference between timesteps
setvals	Set list of old values to new values
setrtoc	Set range to constant
setrtoc2	Set range to constant, others to constant2
gridcellindex	Get grid cell index
const	Create a constant field
random	Create a field with random numbers
topo	Create a field with topography
seq	Create a time series
stdatm	Create values for hydrostatic atmosphere
timsort	Temporal sorting
uvDestag	Destaggering of u/v wind components
rotuvNorth	Rotate u/v wind to North pole
projuvLatLon	Cylindrical Equidistant projection
rotuvb	Backward wind rotation
mrotuvb	Backward rotation of MPIOM data
mastrfu	Mass stream function
pressure_half	Pressure on half-levels
pressure	Pressure on full-levels
delta_pressure	Pressure difference of half-levels
sealevelpressure	Sea level pressure
gheight	Geopotential height on full-levels
gheight_half	Geopotential height on half-levels
air_density	Air density
adisit	Potential temperature to in-situ temperature
adipot	In-situ temperature to potential temperature
rhopot	Calculates potential density
histcount	Histogram count
histsum	Histogram sum
histmean	Histogram mean
histfreq	Histogram frequency
sethalo	Set the bounds of a field
wct	Windchill temperature
fdns	Frost days where no snow index per time period
strwin	Strong wind days index per time period
strbre	Strong breeze days index per time period
strgal	Strong gale days index per time period
hurr	Hurricane days index per time period
cmorlite	CMOR lite
verifygrid	Verify grid coordinates
hpdegrade	Degrade HEALPix grid
hpupgrade	Upgrade HEALPix grid
symmetrize	Mirrors data at the equator
uv2vr_cfd	U and V wind to relative vorticity (curvilinear finite differences)
uv2dv_cfd	U and V wind to divergence (curvilinear finite differences)
cmor	Climate Model Output Rewriting (CMIP-compliant)

Climate Indices (ETCCDI / ECA&D)

Operator	Description
eca_cdd / etccdi_cdd	Consecutive dry days index per time period
eca_cfd	Consecutive frost days index per time period
eca_csu	Consecutive summer days index per time period
eca_cwd / etccdi_cwd	Consecutive wet days index per time period
eca_cwdi	Cold wave duration index wrt mean of reference period
eca_cwfi / etccdi_csdi	Cold-spell days index wrt 10th percentile of reference period
eca_etr	Intra-period extreme temperature range
eca_fd / etccdi_fd	Frost days index per time period
eca_gsl	Thermal growing season length index
eca_hd	Heating degree days per time period
eca_hwdi	Heat wave duration index wrt mean of reference period
eca_hwfi / etccdi_wsdi	Warm spell days index wrt 90th percentile of reference period
eca_id / etccdi_id	Ice days index per time period
eca_r75p	Moderate wet days wrt 75th percentile of reference period
eca_r75ptot	Precipitation percent due to R75p days
eca_r90p	Wet days wrt 90th percentile of reference period
eca_r90ptot	Precipitation percent due to R90p days
eca_r95p	Very wet days wrt 95th percentile of reference period
eca_r95ptot	Precipitation percent due to R95p days
eca_r99p	Extremely wet days wrt 99th percentile of reference period
eca_r99ptot	Precipitation percent due to R99p days
eca_pd / etccdi_r1mm	Precipitation days index per time period
eca_r10mm	Heavy precipitation days index per time period
eca_r20mm	Very heavy precipitation days index per time period
eca_rr1	Wet days index per time period
eca_rx1day / etccdi_rx1day	Highest one day precipitation amount per time period
eca_rx5day / etccdi_rx5day	Highest five-day precipitation amount per time period
eca_sdii	Simple daily intensity index per time period
eca_su / etccdi_su	Summer days index per time period
eca_tg10p	Cold days percent wrt 10th percentile of reference period
eca_tg90p	Warm days percent wrt 90th percentile of reference period
eca_tn10p	Cold nights percent wrt 10th percentile of reference period
eca_tn90p	Warm nights percent wrt 90th percentile of reference period
eca_tr / etccdi_tr	Tropical nights index per time period
eca_tx10p	Very cold days percent wrt 10th percentile of reference period
eca_tx90p	Very warm days percent wrt 90th percentile of reference period
etccdi_tx90p	Percentage of days when daily max temperature > 90th percentile
etccdi_tx10p	Percentage of days when daily max temperature < 10th percentile
etccdi_tn90p	Percentage of days when daily min temperature > 90th percentile
etccdi_tn10p	Percentage of days when daily min temperature < 10th percentile

API Reference

Cdo class

Parameter	Type	Default	Description
cdo_path	str	None	Path to CDO binary (auto-discovered if None)
options	str	""	Default CDO options (e.g. "-O -s -f nc4")
env	dict	None	Custom environment variables
debug	bool	False	Print commands and stderr
timeout	int	None	Default timeout in seconds

Operator method parameters

Parameter	Type	Description
First positional arg	str	Operator parameters (e.g. "0,30,0,30")
input	str or list	Input file(s)
output	str	Output file path
options	str	Additional CDO options for this call
timeout	int	Timeout override
returnXArray	bool	Return as xarray.Dataset
returnCdf	bool	Return as netCDF4.Dataset
returnArray	bool	Return as numpy.ndarray
returnMaArray	bool	Return as masked numpy.ndarray

Utility methods

Method	Returns	Description
cdo.help()	str	General usage summary
cdo.help("operator")	str	CDO help text for a specific operator
cdo.version()	str	CDO version string
cdo.operators()	set	All available CDO operator names
cdo.has_operator(name)	bool	Check if an operator exists
cdo.cleanup()	—	Remove temporary files

CdoError exception

Import: from skyborn_cdo import CdoError

Attribute	Type	Description
returncode	int	CDO exit code
stderr	str	Full CDO error output
cmd	str	Command that failed

Development

git clone --recurse-submodules https://github.com/QianyeSu/skyborn-cdo.git
cd skyborn-cdo
pip install -e ".[test]"
pytest tests/

Author

Qianye Su
Email: suqianye2000@gmail.com
GitHub: @QianyeSu

License

This Python wrapper is licensed under BSD-3-Clause.

CDO itself is licensed under BSD-3-Clause by MPI für Meteorologie. See LICENSE for details.