meso-morphs

Collection of mesoscale cloud morphologies

https://github.com/issi-constrain/meso-morphs

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 3 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (10.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Collection of mesoscale cloud morphologies

Basic Info
  • Host: GitHub
  • Owner: ISSI-CONSTRAIN
  • Language: Python
  • Default Branch: main
  • Size: 73.2 KB
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  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Citation Zenodo

README.md

Mesoscale classifications

DOI

Description

This repository contains a collection of post-processed mesoscale cloud classifications.

Installation of dependencies

The dependencies are listed in environment.yml and can be installed e.g. with conda or mamba: mamba env create -f environment.yml or directly into an existing python environment with mamba install python intake-xarray xarray s3fs "intake<2.0.0" mamba can be downloaded at https://github.com/conda-forge/miniforge/releases/

Usage

To access the mesoscale cloud morphology datasets, the following lines are sufficient (after installing any dependencies):

python import intake cat = intake.open_catalog("https://raw.githubusercontent.com/ISSI-CONSTRAIN/meso-morphs/main/catalog/catalog.yaml") ds = cat.SGFF.to_dask()

Further catalog entries, like those referencing the MCC and MEASURES dataset, can be listed with

list(cat)

Non-python usage

In case the dataset will be handled in a software environment different to python, it might be easiest to store a local copy of the dataset by running the above mentioned python instructions and save the datasets as netCDF files with:

python ds.to_netcdf("SGFF_classifications.nc")

Reproducability / Updating of datasets

  1. To reproduce the workflow, access to the UW olympus cluster is needed
  2. Please set your olympus username as environment variable export SSH_USERNAME=myname
  3. dvc repro downloads the original files and updates the output files
  4. To push files to remote, please export AWS_ACCESS_KEY_ID=${API Key} and export AWS_SECRET_ACCESS_KEY='mysecret'
  5. Only files in the output directory should be pushed with dvc push --remote aws data/output/Daily*

Owner

  • Name: ISSI-CONSTRAIN
  • Login: ISSI-CONSTRAIN
  • Kind: organization

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Eastman
  given-names: Ryan
- family-names: Schulz
  given-names: Hauke
- family-names: McCoy
  given-names: Isabel
- family-names: Wood
  given-names: Rob
date-released: '2024-02-09'
doi: 10.5281/zenodo.10641821
license:
- cc0-1.0
title: Joint Mesoscale Cloud Morphology Dataset
type: dataset
version: v2024.02.1
url: "https://github.com/ISSI-CONSTRAIN/meso-morphs"
preferred-citation:
  type: article
  authors:
  - family-names: Eastman
    given-names: Ryan
  - family-names: Schulz
    given-names: Hauke
  - family-names: McCoy
    given-names: Isabel
  - family-names: Wood
    given-names: Rob
  doi: "https://doi.org/10.5194/egusphere-2023-2118"
  journal: "EGUsphere [preprint]"
  title: "A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies"
  year: 2024

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Dependencies

.github/workflows/validate_zenodo_metadata.yml actions
  • actions/checkout v3 composite
  • walbo/validate-json v1.1.0 composite
environment.yml pypi