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<img src="notebooks/images/logos/ncar_pythia.png" alt="pythia ncar" />

# (Replace_with_your_title) Cookbook
# Geostationary satellite Cookbook

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This Project Pythia Cookbook covers ... (replace `...` with the main subject of your cookbook ... e.g., _working with radar data in Python_)

This **"Pythia Cookbook"** was started during the **Project Pythia June 11-14 2024 in Boulder, CO at the NCAR Mesa Lab**. The **"COOKBOOK GEOSAT"** aims to provide a comprehensive guide for utilizing Satpy to analyze geostationary satellite data of the sensor Advanced Baseline Imager ([ABI](https://www.goes-r.gov/spacesegment/abi.html)) on [GOES-R](https://www.goes-r.gov) (west and east), sensor Advanced Himawari Imager ([AHI](https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/spsg_ahi.html)) on [HIMAWARI](https://www.jma.go.jp/jma/jma-eng/satellite/himawari89.html), and sensor Advance Meteorological Imager (AMI) on [Geo-KOMPSAT-2A](https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.intro) (GK2A). [Satpy](https://satpy.readthedocs.io/en/stable/) is a powerful Python library specifically designed for processing and analyzing satellite data, offering capabilities for data visualization, manipulation, and analysis.

<img align="center" src="notebooks/images/logos/all_sats.png" alt="pythia ncar" />

## Motivation

(Add a few sentences stating why this cookbook will be useful. What skills will you, "the chef", gain once you have reached the end of the cookbook?)
<p>
<img align="left" src="https://registry.opendata.aws/img/logos/noaa-logo.png" width="100" height="100" >
Public access to NOAA's geostationary satellite data, including GOES-16, GOES-17, and GOES-18, is made possible through the NOAA Open Data Dissemination <a href="https://www.noaa.gov/information-technology/open-data-dissemination">NODD</a> Program on commercial cloud platforms like Microsoft Azure, Amazon Web Services, and Google Cloud Platform. Amazon Web Services also hosts data from two other geostationary satellites HIMAWARI and Geo-KOMPSAT-2A. NODD's partnerships with top cloud providers enable near real-time access to to all these satellites, as well as long archives.</p>
<p>
Global weather, climate, and environmental phenomena monitoring is greatly aided by geostationary satellites such as GOES-R, HIMAWARI, and GK2A. The need for thorough tools and resources to efficiently analyze and interpret satellite data is growing as the use of such data for scientific research, weather forecasting, and environmental monitoring grows.
</p>

## Authors
<p>
<img align="left" src="https://avatars.githubusercontent.com/u/13004956?s=200&v=4" width="100" height="100" >
A Python library called Satpy was created specifically for handling data from satellite instruments that observe the Earth. Remote-sensing data can be read, modified, and written with it. Geophysical parameters can be converted from various file formats into Xarray DataArray and Dataset classes, which allow for easy integration with other scientific Python libraries. Satpy combines data from various instrument bands or products to make it easier to create RGB images and composite types. In order to enhance the quality and usefulness of images, it has features for atmospheric corrections and visual improvements. Several formats, including PNG, GeoTIFF, and CF standard NetCDF files, are available for saving output data. Users can resample data to geographic projected grids (areas) using Satpy as well.Although there are already guides and tutorials available for using Satpy, there isn't much thorough advice that is tailored specifically to the analysis
of the three geostationary satellites that can be accessed freely from AWS buckets <a href="https://registry.opendata.aws/noaa-goes/">AWS_GOES-R</a>, <a href="https://registry.opendata.aws/noaa-himawari/">AWS_HIMAWARI</a>, and <a href="https://registry.opendata.aws/noaa-gk2a-pds/">AWS_GK2A</a>. Users can gain access to structured tutorials, detailed instructions, and sample workflows that are specifically designed to meet the needs and distinctive features of these satellites by creating a cookbook specifically for them.</p>

[First Author](@first-author), [Second Author](@second-author), etc. _Acknowledge primary content authors here_
## Authors
| Name | Affiliation |
| ----------- | ----------- |
| [Jorge Bravo](https://github.com/jhbravo) | Stevens Institute of Technology | |
| [Srihari (Hari) Sundar](https://github.com/sriharisundar) | National Renewable Energy Lab |
| [Brian Mapes](https://github.com/brianmapes) | Affiliation University of Miami |
| [Suman Shekhar](https://github.com/Sumanshekhar17) | Rutgers University, The state university of New Jersey |
| [Tri Nguyen](https://github.com/tringuyen180303) | Indiana University Bloomington |
| [Deborah Khider](https://github.com/khider) | University of Southern California |

### Contributors

<a href="https://github.com/ProjectPythia/cookbook-template/graphs/contributors">
<img src="https://contrib.rocks/image?repo=ProjectPythia/cookbook-template" />
<a href="https://github.com/ProjectPythia/cookbook-geosat/graphs/contributors">
<img src="https://contrib.rocks/image?repo=ProjectPythia/cookbook-geosat" />
</a>



## Structure
This development cookbook serves as an example of how to gather, handle, and present various geostationary satellite data types.

### Foundations
The ABI on the GOES-R series, the AHI on the Himawari satellites, and the AMI on the Geo-KOMPSAT-2A satellites all provide multi-channel visibility through their respective 16 spectral bands.

These sensors have several similarities in their spectral band configurations:

(State one or more sections that will comprise the notebook. E.g., _This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."_ Then, describe each section below.)
- All three instruments have bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum.
- The central wavelengths of the spectral bands are comparable across the ABI, AHI, and AMI, enabling similar meteorological and environmental observations.
- The spatial resolutions of the bands also exhibit similarities, with the visible bands typically having finer spatial resolution

### Section 1 ( Replace with the title of this section, e.g. "Foundations" )
### Example workflows

(Add content for this section, e.g., "The foundational content includes ... ")
Several notebooks with the following structure can be found in the notebooks directory:

### Section 2 ( Replace with the title of this section, e.g. "Example workflows" )
- [00_geosat_explaining_steps.ipynb](notebooks/00_geosat_explaining_steps.ipynb):: provides a detailed explanation on how to download data and use Satpy to display it.

(Add content for this section, e.g., "Example workflows include ... ")
Given that you have read the 00_geosat_explaining_steps.ipynb and have a basic understanding of how to use Satpy, the following notebooks are designed without providing an explanation of the various sensors on each satellite.
- [99_auxiliar_dowloading.ipynb](notebooks/99_auxiliar_dowloading.ipynb): In order to run the subsequent notebooks, data must be downloaded from this notebook.
- [01_geosat_ABI_GOES_east.ipynb](notebooks/01_geosat_ABI_GOES_east.ipynb): notebook to read ABI sensor data locally on GOES-east
- [02_geosat_ABI_GOES_west.ipynb](notebooks/02_geosat_ABI_GOES_west.ipynb): notebook to read ABI sensor data locally on GOES-west
- [03_geosat_AHI_HIMAWARI.ipynb](notebooks/03_geosat_AHI_HIMAWARI.ipynb): notebook to read AHI sensor data locally on HIMAWARI
- [04_geosat_AMI_GK2A.ipynb](notebooks/04_geosat_AMI_GK2A.ipynb): notebook for reading AMI sensor data locally on GeoKomposat

## Running the Notebooks

Expand All @@ -51,30 +85,30 @@ on the rocket ship icon, (see figure below), and be sure to select
notebook that you can interact with. I.e. you’ll be able to execute
and even change the example programs. You’ll see that the code cells
have no output at first, until you execute them by pressing
{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
{kbd}`Shift`/+{kbd}`Enter`. Complete details on how to interact with
a live Jupyter notebook are described in [Getting Started with
Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html).

### Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

(Replace "cookbook-example" with the title of your cookbooks)
(Replace "cookbook-geosat" with the title of your cookbooks)

1. Clone the `https://github.com/ProjectPythia/cookbook-example` repository:
1. Clone the `https://github.com/ProjectPythia/cookbook-geosat` repository:

```bash
git clone https://github.com/ProjectPythia/cookbook-example.git
git clone https://github.com/ProjectPythia/cookbook-geosat.git
```

1. Move into the `cookbook-example` directory
1. Move into the `cookbook-geosat` directory
```bash
cd cookbook-example
cd cookbook-geosat
```
1. Create and activate your conda environment from the `environment.yml` file
```bash
conda env create -f environment.yml
conda activate cookbook-example
conda activate cookbook-geosat
```
1. Move into the `notebooks` directory and start up Jupyterlab
```bash
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