reflective-potential
An empirical analysis of Earth's annual-average surface reflectivity potential
Science Score: 36.0%
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○Scientific vocabulary similarity
Low similarity (12.1%) to scientific vocabulary
Keywords
Repository
An empirical analysis of Earth's annual-average surface reflectivity potential
Basic Info
Statistics
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 5
Topics
Metadata Files
README.md
reflective-potential
An empirical analysis of Earth's surface reflectivity potential
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:

Repository Structure
assets- deliverable data and imagesenvironments- conda / mamba environment files for macOS and linuxnotebooks- jupyter notebooks for each step of the workflow01-Ingest- data download from Copernicus Climate Change Service and upload to Google Cloud02-Preprocess- data averaging from hourly-means to annual-means03-Analyze- data transformation through a simple model of reflected radiation04-Validate- replicate results with an independent dataset05-Visualize- data visualization for publicationutils.py- utility functions
CHANGELOG- chronologically ordered list of notable changesCODE_OF_CONDUCT- the code of conduct that contributors and maintainers pledge to followCONTRIBUTING- guidelines for making your own contribution to this projectLICENSE- open source licenseREADME- overview, repo structure, developer setup, and prerequisitesSUPPORT- guidance on how to request help with this project
Developer Setup
- Clone and change directory to the reflective-potential repo.
git clone https://github.com/ReflectiveEarth/reflective-potential.gitcd reflective-potential
- Create and activate the
conda/mambaenvironment 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.ymlconda activate ingest
- e.g. environment for
- Launch Jupyter Lab.
jupyter lab
- 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
- Website: https://reflectiveearth.org
- Twitter: ReflectiveEarth
- Repositories: 1
- Profile: https://github.com/ReflectiveEarth
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
Top Committers
| Name | 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)