Application of Intense QC rainfall measurement quality control methods to DAFNI data
Applies QC methods to rainfall guage data
Amy Green, Newcastle University (amy.green3@newcastle.ac.uk)
Elizabeth Lewis, Newcastle University (elizabeth.lewis2@newcastle.ac.uk)
Robin Wardle
RSE Team, Newcastle Data
Newcastle University NE1 7RU
(robin.wardle@newcastle.ac.uk)
Other required tools: tar, zip.
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A local Docker container that mounts the test data can be built and executed using:
docker build . -t pyramid-intense-qc -f Dockerfile
docker run -v "$(pwd)/data:/data" pyramid-intense-qc
Note that output from the container, placed in the ./data subdirectory, will have root ownership as a result of the way in which Docker's access permissions work.
The model is containerised using Docker, and the image is tar'ed and zip'ed for uploading to DAFNI. Use the following commands in a *nix shell to accomplish this.
docker build . -t pyramid-intense-qc -f Dockerfile
docker save -o pyramid-intense-qc.tar pyramid-intense-qc:latest
gzip pyramid-intense-qc.tar
The pyramid-intense-qc.tar.gz Docker image and accompanying DAFNI model definintion file (model-definition.yaml) can be uploaded as a new model using the "Add model" facility at https://facility.secure.dafni.rl.ac.uk/models/.
The deployed model can be run in a DAFNI workflow. See the DAFNI workflow documentation for details.
- Initial Research
- Minimum viable product <-- You are Here
- Alpha Release
- Feature-Complete Release
The PYRAMID project has ended. All pull requests will be ignored.
TBD
This work was funded by NERC, grant ref. NE/V00378X/1, “PYRAMID: Platform for dYnamic, hyper-resolution, near-real time flood Risk AssessMent Integrating repurposed and novel Data sources”. See the project funding URL.