climalab 4.5.2

A Python toolkit for climate data processing and analysis

climalab

climalab is a Python toolkit designed to facilitate climate data analysis and manipulation, including tools for data extraction, processing, and visualisation. It leverages external tools and standards like CDO (Climate Data Operators), NCO (NetCDF operators), and CDS (Copernicus Climate Data Store) to streamline workflows for climate-related research.

Features

• Meteorological Tools:

  • Comprehensive handling of meteorological variables and data
  • Unit conversions (temperature, wind speed, angles)
  • Wind direction calculations using meteorological criteria
  • Dewpoint temperature and relative humidity calculations using Magnus' formula
  • Weather software input file generation (EnergyPlus EPW format)

• NetCDF Tools:

  • Advanced CDO operations for netCDF file manipulation (merge, remap, statistical operations)
  • NCO tools for efficient data processing and variable modifications
  • Faulty file detection and reporting
  • Basic information extraction from netCDF files (lat/lon bounds, time information)
  • Time coordinate manipulation and correction tools

• Supplementary Analysis Tools:

  • Visualisation tools for maps and basic plots
  • Bias correction methods (parametric and non-parametric quantile mapping)
  • Statistical analysis and evaluation tools
  • Auxiliary functions for data processing and plotting

• Data Analysis Project Templates:

  • Sample project structure with configuration-based approach
  • Automated data download scripts for CORDEX, E-OBS, ERA5, and ERA5-Land datasets
  • YAML configuration files for different climate datasets
  • Standardised directory organisation for climate data projects

Installation

Prerequisites

Before installing, please ensure the following dependencies are available on your system:

• External Tools (required for full functionality):

  • CDO (Climate Data Operators) - for netCDF processing
  • NCO (NetCDF Operators) - for netCDF manipulation

• Required Third-Party Libraries:

  pip install numpy pandas scipy cdsapi PyYAML xarray netCDF4
  

  Or via Anaconda (recommended channel: conda-forge):

  conda install -c conda-forge numpy pandas scipy cdsapi pyyaml xarray netcdf4
  

• Internal Package Dependencies:

  pip install filewise paramlib pygenutils
  

Installation (from PyPI)

Install the package using pip:

pip install climalab

Development Installation

For development purposes, you can install the package in editable mode:

git clone https://github.com/yourusername/climalab.git
cd climalab
pip install -e .

Usage

Basic Example - Meteorological Variables

from climalab.meteorological import variables
import numpy as np

# Convert temperature from Kelvin to Celsius using angle converter for degrees
temp_kelvin = np.array([273.15, 283.15, 293.15])
# Convert wind speeds
wind_mps = 10.0
wind_kph = variables.ws_unit_converter(wind_mps, "mps_to_kph")
print(f"Wind speed: {wind_mps} m/s = {wind_kph} km/h")

# Calculate dewpoint temperature
temperature = np.array([20, 25, 30])  # °C
relative_humidity = np.array([60, 70, 80])  # %
dewpoint = variables.dewpoint_temperature(temperature, relative_humidity)
print(f"Dewpoint temperatures: {dewpoint}")

Advanced Example - NetCDF Processing

from climalab.netcdf_tools import cdo_tools
from climalab.netcdf_tools.detect_faulty import scan_ncfiles

# Merge multiple NetCDF files with time steps
file_list = ['temp_2000.nc', 'temp_2001.nc', 'temp_2002.nc']
cdo_tools.cdo_mergetime(
    file_list=file_list,
    variable='temperature',
    freq='daily',
    model='ERA5',
    experiment='reanalysis',
    calc_proc='mergetime',
    period='2000-2002',
    region='global',
    ext='nc'
)

# Select specific years from a dataset
cdo_tools.cdo_selyear(
    file_list=['climate_data_full.nc'],
    selyear_str='2000/2010',
    freq='monthly',
    model='CORDEX',
    experiment='historical',
    calc_proc='subset',
    region='europe',
    ext='nc'
)

# Detect faulty NetCDF files
scan_ncfiles('/path/to/netcdf/files')

Bias Correction Example

from climalab.supplementary_tools import auxiliary_functions
import numpy as np

# Generate sample data
obs_data = np.random.normal(25, 3, 1000)  # observed temperature data
sim_data = np.random.normal(27, 4, 1000)  # simulated temperature data

# Apply bias correction using delta method
obs_mean = np.mean(obs_data)
sim_mean = np.mean(sim_data)
corrected_data = auxiliary_functions.ba_mean(sim_data, sim_mean, obs_mean)

# Apply quantile mapping
corrected_qm = auxiliary_functions.ba_nonparametric_qm(
    sim_data, sim_data, obs_data
)

Data Download Example

# The data_analysis_projects_sample provides ready-to-use scripts
# for downloading climate data with configuration files:

# 1. Configure your dataset in the YAML files (config/)
# 2. Run the download scripts:
from climalab.data_analysis_projects_sample.src.data import download_era5
# download_era5.main()  # Downloads ERA5 data based on configuration

Project Structure

The package is organised into several sub-packages:

climalab/
├── meteorological/
│   ├── variables.py           # Unit conversions, meteorological calculations
│   └── weather_software.py    # EnergyPlus weather file generation
├── netcdf_tools/
│   ├── cdo_tools.py          # CDO operations and wrappers
│   ├── nco_tools.py          # NCO operations and wrappers
│   ├── detect_faulty.py      # NetCDF file integrity checking
│   └── extract_basics.py     # Basic information extraction
├── supplementary_tools/
│   ├── auxiliary_functions.py    # Bias correction and utility functions
│   ├── ba_*.py                   # Individual bias correction methods
│   ├── basic_*.py                # Basic plotting functions
│   ├── comparison_lineplot.py    # Comparison plotting tools
│   ├── temperature_map.py        # Temperature mapping tools
│   └── eval_original.py          # Evaluation and statistics
└── data_analysis_projects_sample/
    ├── config/                   # YAML configuration files
    │   ├── cordex_config.yaml
    │   ├── eobs_config.yaml
    │   ├── era5_config.yaml
    │   └── era5_land_config.yaml
    ├── src/data/                 # Data download scripts
    │   ├── cds_tools.py
    │   ├── download_cordex.py
    │   ├── download_eobs.py
    │   ├── download_era5.py
    │   └── download_era5_land.py
    └── data/                     # Data storage directories
        ├── raw/
        └── processed/

Key Functions

Meteorological Tools

• angle_converter() - Convert between degrees and radians
• ws_unit_converter() - Convert wind speeds between m/s and km/h
• dewpoint_temperature() - Calculate dewpoint using Magnus' formula
• relative_humidity() - Calculate relative humidity from temperature and dewpoint
• meteorological_wind_direction() - Calculate wind direction from u/v components

NetCDF Tools (CDO)

• cdo_mergetime() - Merge files with different time steps
• cdo_selyear() - Select specific years from datasets
• cdo_sellonlatbox() - Extract geographical regions
• cdo_remap() - Remap data to different grids
• cdo_periodic_statistics() - Calculate temporal statistics

NetCDF Tools (NCO)

• modify_variable_units_and_values() - Modify variable values and units
• modify_coordinate_values_by_threshold() - Conditional coordinate modifications
• modify_coordinate_all_values() - Apply operations to all coordinate values

Bias Correction

• ba_mean() - Delta (mean bias) correction
• ba_mean_and_var() - Mean and variance correction
• ba_nonparametric_qm() - Non-parametric quantile mapping
• ba_parametric_qm() - Parametric quantile mapping

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Climate Data Operators (CDO) team
• Copernicus Climate Data Store (CDS)
• NetCDF Operators (NCO) team
• Potsdam Institute for Climate Impact Research (sample bias correction methods)

Contact

For any questions or suggestions, please open an issue on GitHub or contact the maintainers.

Version

Current version: 4.5.1

For detailed changelog, see CHANGELOG.md. For versioning information, see VERSIONING.md.