Containerized application for running individual tasks in a larger, orchestrated CMIP6 bias-adjustment and downscaling workflow.
This is under heavy development.
Commands can be run through the command line with dodola <command>.
Commands:
adjust-maximum-precipitation Adjust maximum precipitation in a dataset
apply-dtr-floor Apply a floor to diurnal temperature...
apply-non-polar-dtr-ceiling Apply a ceiling to diurnal temperature...
apply-qdm Adjust simulation year with quantile...
apply-qplad Adjust (downscale) simulation year with...
cleancmip6 Clean up and standardize GCM
correct-wetday-frequency Correct wet day frequency in a dataset
get-attrs Get attrs from data
prime-qdm-output-zarrstore Prime a Zarr Store for regionally-written...
prime-qplad-output-zarrstore Prime a Zarr Store for regionally-written...
rechunk Rechunk Zarr store in memory.
regrid Spatially regrid a Zarr Store in memory
removeleapdays Remove leap days and update calendar
train-qdm Train quantile delta mapping (QDM)
train-qplad Train Quantile-Preserving, Localized...
validate-dataset Validate a CMIP6, bias corrected or...
See dodola --help or dodola <command> --help for more information.
From the command line, run one of the downscaling workflow's validation steps with:
dodola validate-dataset "gs://your/climate/data.zarr" \
--variable "tasmax" \
--data-type "downscaled" \
-t "historical"The service used by this command can be called directly from a Python session or script
import dodola.services
dodola.services.validate(
"gs://your/climate/data.zarr",
"tasmax",
data_type="downscaled",
time_period="historical",
)dodola is generally run from within a container. dodola container images are currently hosted at ghcr.io/climateimpactlab/dodola.
Alternatively, you can install a bleeding-edge version of the application and access the command-line interface or Python API with pip:
pip install git+https://github.com/ClimateImpactLab/dodolaBecause there are many compiled dependencies we recommend installing dodola and its dependencies within a conda virtual environment. Dependencies used in the container to create its conda environment are in ./environment.yaml.
Additional technical documentation is available online at https://climateimpactlab.github.io/dodola/.
Source code is available online at https://github.com/ClimateImpactLab/dodola. This software is Open Source and available under the Apache License, Version 2.0.