python-cmethods

Welcome to python-cmethods, a powerful Python package designed for bias correction and adjustment of climate data. Built with a focus on ease of use and efficiency, python-cmethods offers a comprehensive suite of functions tailored for applying bias correction methods to climate model simulations and observational datasets via command-line interface and API.

Please cite this project as described in https://zenodo.org/doi/10.5281/zenodo.7652755.

Table of Contents

1. About
2. Available Methods
3. Installation
4. Usage and Examples
5. Notes
6. Contribution
7. References

1. About

Bias correction in climate research involves the adjustment of systematic errors or biases present in climate model simulations or observational datasets to improve their accuracy and reliability, ensuring that the data better represents actual climate conditions. This process typically involves statistical methods or empirical relationships to correct for biases caused by factors such as instrument calibration, spatial resolution, or model deficiencies.

Figure 1: Schematic representation of a bias adjustment procedure

python-cmethods empowers scientists to effectively address those biases in climate data, ensuring greater accuracy and reliability in research and decision-making processes. By leveraging cutting-edge techniques and seamless integration with popular libraries like xarray and Dask, this package simplifies the process of bias adjustment, even when dealing with large-scale climate simulations and extensive spatial domains.

In this way, for example, modeled data, which on average represent values that are too cold, can be easily bias-corrected by applying any adjustment procedure included in this package.

For instance, modeled data can report values that are way colder than the those data reported by reanalysis time-series. To address this issue, an adjustment procedure can be employed. The figure below illustrates the observed, modeled, and adjusted values, revealing that the delta-adjusted time series ($T^{*DM}{sim,p}$) is significantly more similar to the observational data ($T{obs,p}$) than the raw model output ($T{sim,p}$).

Figure 2: Temperature per day of year in observed, modeled, and bias-adjusted climate data

The mathematical foundations supporting each bias correction technique implemented in python-cmethods are integral to the package, ensuring transparency and reproducibility in the correction process. Each method is accompanied by references to trusted publications, reinforcing the reliability and rigor of the corrections applied.

2. Available Methods

python-cmethods provides the following bias correction techniques:

• Linear Scaling
• Variance Scaling
• Delta Method
• Quantile Mapping
• Detrended Quantile Mapping
• Quantile Delta Mapping

Please refer to the official documentation for more information about these methods as well as sample scripts: https://python-cmethods.readthedocs.io/en/stable/

Best Practices and important Notes

• The training data should have the same temporal resolution.

• Except for the variance scaling, all methods can be applied on stochastic and non-stochastic climate variables. Variance scaling can only be applied on non-stochastic climate variables.

  • Non-stochastic climate variables are those that can be predicted with relative certainty based on factors such as location, elevation, and season. Examples of non-stochastic climate variables include air temperature, air pressure, and solar radiation.

  • Stochastic climate variables, on the other hand, are those that exhibit a high degree of variability and unpredictability, making them difficult to forecast accurately. Precipitation is an example of a stochastic climate variable because it can vary greatly in timing, intensity, and location due to complex atmospheric and meteorological processes.

• Except for the detrended quantile mapping (DQM) technique, all methods can be applied to 1- and 3-dimensional data sets. The implementation of DQM to 3-dimensional data is still in progress.

• Except for DQM, all methods can be applied using cmethods.adjust. Chunked data for computing e.g. in a dask cluster is possible as well.

• For any questions -- please open an issue at https://github.com/btschwertfeger/python-cmethods/issues

3. Installation

If the installation fails due to missing HDF5 headers, ensure that 'hdf5' and 'netcdf' are pre-installed, e.g. on macOS using: brew install hdf5 netcdf.

python3 -m pip install python-cmethods

The package is also available via conda-forge. See conda-forge/python_cmethods for more information.

4. CLI Usage

The python-cmethods package provides a command-line interface for applying various bias correction methods out of the box.

Keep in mind that due to the various kinds of data and possibilities to pre-process those, the CLI only provides a basic application of the implemented techniques. For special parameters, adjustments, and data preparation, please use programming interface.

Listing the parameters and their requirements is available by passing the --help option:

cmethods --help

Applying the cmethods tool on the provided example data using the linear scaling approach is shown below:

cmethods \
  --obs examples/input_data/observations.nc \
  --simh examples/input_data/control.nc \
  --simp examples/input_data/scenario.nc \
  --method linear_scaling \
  --kind add \
  --variable tas \
  --group time.month \
  --output linear_scaling.nc

2024/04/08 18:11:12     INFO | Loading data sets ...
2024/04/08 18:11:12     INFO | Data sets loaded ...
2024/04/08 18:11:12     INFO | Applying linear_scaling ...
2024/04/08 18:11:15     INFO | Saving result to linear_scaling.nc ...

For applying a distribution-based bias correction technique, the following example may help:

cmethods \
  --obs examples/input_data/observations.nc \
  --simh examples/input_data/control.nc \
  --simp examples/input_data/scenario.nc \
  --method quantile_delta_mapping \
  --kind add \
  --variable tas \
  --quantiles 1000 \
  --output quantile_delta_mapping.nc

2024/04/08 18:16:34     INFO | Loading data sets ...
2024/04/08 18:16:35     INFO | Data sets loaded ...
2024/04/08 18:16:35     INFO | Applying quantile_delta_mapping ...
2024/04/08 18:16:35     INFO | Saving result to quantile_delta_mapping.nc ...

5. Programming Interface Usage and Examples

import xarray as xr
from cmethods import adjust

obsh = xr.open_dataset("input_data/observations.nc")
simh = xr.open_dataset("input_data/control.nc")
simp = xr.open_dataset("input_data/scenario.nc")

# adjust only one grid cell
ls_result = adjust(
    method="linear_scaling",
    obs=obsh["tas"][:, 0, 0],
    simh=simh["tas"][:, 0, 0],
    simp=simp["tas"][:, 0, 0],
    kind="+",
    group="time.month",
)

# adjust all grid cells
qdm_result = adjust(
    method="quantile_delta_mapping",
    obs=obsh["tas"],
    simh=simh["tas"],
    simp=simp["tas"],
    n_quantiles=1000,
    kind="+",
)

# to calculate the relative rather than the absolute change,
# '*' can be used instead of '+' (this is preferred when adjusting
# stochastic variables like precipitation)

It is also possible to adjust chunked data sets. Feel free to have a look into tests/test_zarr_dask_compatibility.py to get a starting point.

Notes:

• For the multiplicative techniques a maximum scaling factor of 10 is defined. This can be changed by passing the optional parameter max_scaling_factor.
• Except for detrended quantile mapping, all implemented techniques can be applied to single and multi-dimensional data sets by executing the cmethods.adjust function.
• A Jupyter notebook applying all those methods is provided here: /examples/examples.ipynb
• The example data is located at: /examples/input_data/*.nc

5. Notes

• Computation in Python takes some time, so this is only for demonstration. When adjusting large datasets, you should either use chunked data using for example a dask cluster or to apply the command-line tool BiasAdjustCXX.
• Formulas and references can be found in the implementations of the corresponding functions, on the bottom of the README.md and in the documentation.

Space for improvements

• Since the scaling methods implemented so far scale by default over the mean values of the respective months, unrealistic long-term mean values may occur at the month transitions. This can be prevented either by selecting group='time.dayofyear'. Alternatively, it is possible not to scale using long-term mean values, but using a 31-day interval, which takes the 31 surrounding values over all years as the basis for calculating the mean values. This is not yet implemented, because even the computation for this takes so much time, that it is not worth implementing it in python - but this is available in BiasAdjustCXX.

6. 🆕 Contributions

… are welcome but:

• First check if there is an existing issue or PR that addresses your problem/solution. If not - create one first - before creating a PR.
• Typo fixes, project configuration, CI, documentation or style/formatting PRs will be rejected. Please create an issue for that.
• PRs must provide a reasonable, easy to understand and maintain solution for an existing problem. You may want to propose a solution when creating the issue to discuss the approach before creating a PR.
• There is currently no need for the implementation of further bias correction methods.

7. References

• Schwertfeger, Benjamin Thomas and Lohmann, Gerrit and Lipskoch, Henrik (2023) "Introduction of the BiasAdjustCXX command-line tool for the application of fast and efficient bias corrections in climatic research", SoftwareX, Volume 22, 101379, ISSN 2352-7110, (https://doi.org/10.1016/j.softx.2023.101379)
• Schwertfeger, Benjamin Thomas (2022) "The influence of bias corrections on variability, distribution, and correlation of temperatures in comparison to observed and modeled climate data in Europe" (https://epic.awi.de/id/eprint/56689/)
• Linear Scaling and Variance Scaling based on: Teutschbein, Claudia and Seibert, Jan (2012) "Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods" (https://doi.org/10.1016/j.jhydrol.2012.05.052)
• Delta Method based on: Beyer, R. and Krapp, M. and Manica, A.: "An empirical evaluation of bias correction methods for palaeoclimate simulations" (https://doi.org/10.5194/cp-16-1493-2020)
• Quantile and Detrended Quantile Mapping based on: Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock "Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?" (https://doi.org/10.1175/JCLI-D-14-00754.1)
• Quantile Delta Mapping based on: Tong, Y., Gao, X., Han, Z. et al. "Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods". Clim Dyn 57, 1425–1443 (2021). (https://doi.org/10.1007/s00382-020-05447-4)
• I'd like to express my gratitude to @riley-brady for initiating and contributing to the discussion on #47. I appreciate all the valuable suggestions provided throughout the implementation of the subsequent changes.