reflective-potential

An empirical analysis of Earth's annual-average surface reflectivity potential

https://github.com/ReflectiveEarth/reflective-potential

Science Score: 36.0%

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  • Academic publication links
    Links to: zenodo.org
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    Low similarity (12.1%) to scientific vocabulary

Keywords

albedo albedo-maps climate climate-data climate-science
Last synced: 6 months ago · JSON representation

Repository

An empirical analysis of Earth's annual-average surface reflectivity potential

Basic Info
  • Host: GitHub
  • Owner: ReflectiveEarth
  • License: bsd-3-clause-clear
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 241 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 5
Topics
albedo albedo-maps climate climate-data climate-science
Created over 4 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog Contributing Funding License Code of conduct Support

README.md

reflective-potential

An empirical analysis of Earth's surface reflectivity potential

DOI

Contains modified Copernicus Climate Change Service information obtained in 2021. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

Reflective Earth is on a mission to slow global warming as fast and safely as possible by increasing Earth's reflectivity to reduce its energy imbalance. Reflectivity interventions reduce the amount of sunlight absorbed by the Earth system, i.e. the amount of energy entering the system. Deploying reflective materials as a stop gap could limit the amount of warming experienced by people and buy society time to reduce greenhouse gas emissions and drawdown atmospheric greenhouse gas concentrations.

The potential of reflective materials to reflect sunlight strongly depends on location. The amount of incoming solar radiation varies greatly, with more being received in the tropics and less being received at the poles. Clouds, water vapor, and aerosols (e.g. dust, smoke) scatter and absorb sunlight. These properties vary spatially as well.

This code repository contains workflows to estimate the potential of Earth's surface to reflect incoming sunlight back out to space. We use data from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis product (ERA5) and National Aeronautics and Space Administration (NASA) Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) satellite-derived product, specifically radiative fluxes at the surface and top of atmosphere. This allows us to estimate surface reflectance and atmospheric transmittance and reflectance. When averaged over several decades, these properties can be combined with incoming solar radiation and surface albedo to model the potential surface-reflected outgoing solar radiation:

ROM

Repository Structure

  • assets - deliverable data and images
  • environments - conda / mamba environment files for macOS and linux
  • notebooks - jupyter notebooks for each step of the workflow
    • 01-Ingest - data download from Copernicus Climate Change Service and upload to Google Cloud
    • 02-Preprocess - data averaging from hourly-means to annual-means
    • 03-Analyze - data transformation through a simple model of reflected radiation
    • 04-Validate - replicate results with an independent dataset
    • 05-Visualize - data visualization for publication
    • utils.py - utility functions
  • CHANGELOG - chronologically ordered list of notable changes
  • CODE_OF_CONDUCT - the code of conduct that contributors and maintainers pledge to follow
  • CONTRIBUTING - guidelines for making your own contribution to this project
  • LICENSE - open source license
  • README - overview, repo structure, developer setup, and prerequisites
  • SUPPORT - guidance on how to request help with this project

Developer Setup

  1. Clone and change directory to the reflective-potential repo.
    • git clone https://github.com/ReflectiveEarth/reflective-potential.git
    • cd reflective-potential
  2. Create and activate the conda/mamba environment corresponding to the notebook you would like to run.
    • e.g. environment for 01-ingest.ipynb
      • {conda | mamba} env create --file environment/{linux | macos}.ingest.environment.yml
      • conda activate ingest
  3. Launch Jupyter Lab.
    • jupyter lab
  4. Open and run the notebooks in the eponymous directory.
    • N.B. additional setup may be required. See the Preliminaries section of each notebook.

Prerequisites

  • A Google Account in order to access Google Cloud Platform.
  • A Google Cloud project with billing enabled. Requester Pays is turned on for all Google Cloud Storage buckets in this repo. Google Cloud Storage requests will incur charges.
  • Optionally, conda or mamba to manage package dependencies.
  • Optionally, one or more Google Cloud Storage buckets to store project data.
  • Optionally, a Copernicus Climate Data Store Account to ingest C3S data.

Support

Read the support guidelines for guidance on how to reach out for help with this project.

Contributing

We welcome contributions that improve the quality of our code and/or science. Before you dive in, read the contribution guidelines.

Code of Conduct

This project has a code of conduct. By interacting with this repository, organization, or community you agree to abide by its terms.

License

Clear BSD © 2021-2024 Reflective Earth

Owner

  • Name: Reflective Earth
  • Login: ReflectiveEarth
  • Kind: organization
  • Location: Earth

We're on a mission to slow global warming right now! With innovations small and large, we can reduce global warming by increasing Earth's reflectivity.

GitHub Events

Total
  • Issues event: 3
  • Watch event: 2
  • Push event: 5
  • Pull request event: 2
  • Create event: 3
Last Year
  • Issues event: 3
  • Watch event: 2
  • Push event: 5
  • Pull request event: 2
  • Create event: 3

Committers

Last synced: 6 months ago

All Time
  • Total Commits: 34
  • Total Committers: 3
  • Avg Commits per committer: 11.333
  • Development Distribution Score (DDS): 0.147
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Brian Smoliak b****n@r****g 29
Brian Smoliak b****k@g****m 4
LNSY 4****e@u****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 10
  • Total pull requests: 14
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 17 days
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 14
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: 8 months
  • Average time to close pull requests: 8 months
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • bsmoliak (10)
Pull Request Authors
  • bsmoliak (14)
  • lindseyjohnasterius (1)
Top Labels
Issue Labels
enhancement (5) bug (3) documentation (2)
Pull Request Labels
enhancement (9) bug (4) documentation (3)