python-cmethods 0.5.2

Collection of bias adjustment procedures for multidimensional climate data

Bias-Adjustment-Python

Collection of different scale- and distribution-based bias adjustment techniques for climatic research. (see examples.ipynb for help)

Bias adjustment procedures in Python are very slow, so they should not be used on large data sets. A C++ implementation that works way faster can be found here: https://github.com/btschwertfeger/Bias-Adjustment-Cpp.

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Available methods:

• Linear Scaling (additive and multiplicative)
• Variance Scaling (additive)
• Delta (Change) Method (additive and multiplicative)
• Quantile Mapping (additive)
• Detrended Quantile Mapping (additive and multiplicative)
• Quantile Delta Mapping (additive and multuplicative)

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Usage

Installation

python3 -m pip install python-cmethods

Import and application

import xarray as xr
from cmethods.CMethods import CMethods
cm = CMethods()

obsh = xr.open_dataset('input_data/obs.nc')
simh = xr.open_dataset('input_data/contr.nc')
simp = xr.open_dataset('input_data/scen.nc')

ls_result = cm.linear_scaling(
    obs = obsh['tas'][:,0,0],
    simh = simh['tas'][:,0,0],
    simp = simp['tas'][:,0,0],
    kind = '+' # *
)

qdm_result = cm.adjust_2d( # 2d = 2 spatial and 1 time dimension
    method = 'quantile_delta_mapping',
    obs = obsh['tas'],
    simh = simh['tas'],
    simp = simp['tas'],
    n_quaniles = 1000,
    kind = '+' # *
)
# * to calculate the relative rather than the absolute change, 
# '*' can be used instead of '+' (this is prefered when adjusting
# ratio based variables like precipitation)

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Examples (see repository on GitHub)

/examples/examples.ipynb: Notebook containing different methods and plots

/examples/do_bias_correction.py: Example script for adjusting climate data

python3 do_bias_correction.py   \
    --obs input_data/obs.nc     \
    --contr input_data/contr.nc \
    --scen input_data/scen.nc   \
    --method linear_scaling     \
    --variable tas              \
    --unit '°C'                 \
    --group time.month          \
    --kind + 

• Linear and variance, as well as delta change method require --group time.month as argument.
• Adjustment methods that apply changes in distributional biasses (QM, QDM, DQM; EQM, ...) need the --nquantiles argument set to some integer.
• Data sets should have the same spatial resolutions.

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Notes:

• Computation in Python takes some time, so this is only for demonstration. When adjusting large datasets, its best to the C++ implementation mentioned above.

References

• 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)