Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

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

An empirical analysis of Earth's annual-average surface reflectivity potential
https://github.com/ReflectiveEarth/reflective-potential

albedo albedo-maps climate climate-data climate-science

Last synced: 3 months ago
JSON representation

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

Awesome Lists containing this project

README 

        
# reflective-potential



### An empirical analysis of Earth's surface reflectivity potential



[![DOI](https://zenodo.org/badge/373627024.svg)](https://zenodo.org/badge/latestdoi/373627024)



> Contains modified [Copernicus Climate Change Service][copernicus]

> 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][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][conda] or [mamba][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][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][contributing] guidelines.



## Code of Conduct



This project has a [code of conduct][conduct]. By interacting with this

repository, organization, or community you agree to abide by its terms.



## License



[Clear BSD][license] © 2021-2022 [Reflective Earth][author]



[author]: https://www.reflectiveearth.org

[conduct]: CODE_OF_CONDUCT.md

[conda]: https://docs.conda.io/en/latest/miniconda.html

[contributing]: CONTRIBUTING.md

[copernicus]: https://climate.copernicus.eu/

[license]: LICENSE.md

[mamba]: https://mamba.readthedocs.io/en/latest/

[rom]: assets/ROM_v042.png

[support]: SUPPORT.md