Containerized application for running the steps in a larger, orchestrated CMIP6 bias-correction 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, you replicate one of the downscaling workflow's validation steps with:
dodola validate-dataset <InsertZarrStoreURLhere> \
--variable "tasmax" \
--data-type "downscaled" \
-t historical
Alternatively, the service used by this command can be called directly from Python in dodola.services
.
dodola
is generally run from within a container. But, you can install the application and access the command-line interface or Python API with pip
:
pip install git+https://github.com/ClimateImpactLab/dodola
the dependencies used in the container are in ./environment.yaml
.
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.