qqscale

This directory contains command line programs for applying quantile scaling.

Methods

The programs in this repository use the bias adjustment and downscaling functionality in xclim to apply quantile scaling.

Depending on the context, there are two different ways the programs can be used:

• To apply quantile delta changes (QDC) between an historical and future model simulation either additively or multiplicatively to observational data in order to produce climate projection data for a future time period.
• To remove quantile biases between an historical model simulation and observations from model data in order to produce bias corrected model data. This has been referred to as equidistant CDF matching (EDCDFm) in the case of additive bias correction or equiratio CDF matching (EQCDFm) in the case of multiplicative bias correction.

See docs/method_ecdfm.md and docs/method_qdc.md for a detailed description of these methods and how they are implemented in the qqscale software.

Software environment

The scripts in this respository depend on the following Python libraries: netCDF4, xclim, xesmf, cmdline_provenance, gitpython, and pytest (if running the tests). A copy of the scripts and a software environment with those libraries installed can be accessed or created in a number of ways (see below):

For members of the Australian Climate Service...

If you're a member of the xv83 project on NCI (i.e. if you're part of the Australian Climate Service), you have access the code and an appropriate conda environment at /g/data/xv83/quantile-mapping.

You can therefore run the scripts as follows. e.g.:

$ /g/data/xv83/quantile-mapping/miniconda3/envs/qq-workflows/bin/python /g/data/xv83/quantile-mapping/qqscale/adjust.py -h

For everyone else...

If you don't have access to a Python environment with the required packages pre-installed you'll need to create your own. For example:

$ conda install -c conda-forge netCDF4 xclim=0.36.0 pint=0.19.2 xesmf cmdline_provenance gitpython

You can then clone this GitHub repository and run the help option on one of the command line programs to check that everything is working. For example:

$ git clone [email protected]:AusClimateService/qqscale.git
$ cd qqscale
$ python adjust.py -h

Data processing

Command line

At the command line, QDC and/or ECDFm can be achieved by running the following scripts:

1. train.py to calculate the adjustment factors between an historical model dataset and a reference dataset (for QDC the reference dataset is a future model simulation; for ECDFm it is observations)
2. adjust.py to apply the adjustment factors to the target data (for QDC the target data is observations; for ECDFm it is a model simulation)

See the files named docs/example_*.md for detailed worked examples using these two command line programs.

Various command line workflows that use the qqscale software can be found at:
https://github.com/AusClimateService/qq-workflows

Jupyter notebook

Starting with historical (ds_hist), reference (ds_ref) and target (ds_target) xarray Datasets containing the variable of interest (hist_var, ref_var and target_var) you can import the relevant functions from the scripts mentioned above. For instance, a typical workflow would look something like this:

import train
import adjust


ds_adjust = train.train(
    ds_hist,
    ds_ref,
    hist_var,
    ref_var,
    scaling='additive',  # use multiplicative for precip data
    time_grouping='monthly',
    nquantiles=100,
    ssr=False,  # Use True for precip data
)
ds_qq = adjust.adjust(
    ds_target,
    target_var,
    ds_adjust,
    ssr=False,  # Use True for precip data
    ref_time=True,
    interp='nearest', 
)

Questions

Questions or comments are welcome at the GitHub repostory associated with the code:
https://github.com/AusClimateService/qqscale/issues