dodola

Containerized application for running individual tasks in a larger, orchestrated CMIP6 bias-adjustment and downscaling workflow.

This is under heavy development.

Features

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.

Example

From the command line, run one of the downscaling workflow's validation steps with:

shell 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

```python import dodola.services

dodola.services.validate( "gs://your/climate/data.zarr", "tasmax", datatype="downscaled", timeperiod="historical", ) ```

Installation

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:

shell pip install git+https://github.com/ClimateImpactLab/dodola

Because 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.

Support

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.