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Download and display GOES-East and GOES-West data

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GOES-East and GOES-West satellite data are made available on Amazon Web Services through NOAA's Open Data Dissemination Program. GOES-2-go is a python package that makes it easy to find and download the files you want from AWS to your local computer with some additional helpers to visualize and understand the data.

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📔 GOES-2-go Documentation

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Installation

The easiest way to install goes2go and its dependencies is with Conda from conda-forge.

conda install -c conda-forge goes2go

You may also create the provided Conda environment, environment.yml.

# Download environment file
wget https://github.com/blaylockbk/goes2go/raw/main/environment.yml

# Modify that file if you wish.

# Create the environment
conda env create -f environment.yml

# Activate the environment
conda activate goes2go

Alternatively, goes2go is published on PyPI and you can install it with pip, but it requires some additional dependencies that you will have to install yourself:

When those are installed within your environment, then you can install GOES-2-go with pip.

# Latest published version
pip install goes2go

# ~~ or ~~

# Most recent changes
pip install git+https://github.com/blaylockbk/goes2go.git

Capabilities

  graph TD;
      aws16[(AWS\nnoaa-goes16)] -.-> G
      aws17[(AWS\nnoaa-goes17)] -.-> G
      aws18[(AWS\nnoaa-goes18)] -.-> G
      G((. GOES 2-go .))
      G --- .latest
      G --- .nearesttime
      G --- .timerange
      .latest --> ds[(xarray.DataSet)]
      .nearesttime --> ds[(xarray.DataSet)]
      .timerange --> ds[(xarray.DataSet)]
      ds --- rgb[ds.rgb\naccessor to make RGB composites]
      ds --- fov[ds.FOV\naccessor to get field-of-view polygons]

      style G fill:#F8AF22,stroke:#259DD7,stroke-width:4px,color:#000000

Download Data

Download GOES ABI or GLM NetCDF files to your local computer. Files can also be read with xarray.

First, create a GOES object to specify the satellite, data product, and domain you are interested in. The example below downloads the Multi-Channel Cloud Moisture Imagery for CONUS.

from goes2go import GOES

# ABI Multi-Channel Cloud Moisture Imagry Product
G = GOES(satellite=16, product="ABI-L2-MCMIP", domain='C')

# Geostationary Lightning Mapper
G = GOES(satellite=17, product="GLM-L2-LCFA", domain='C')

# ABI Level 1b Data
G = GOES(satellite=17, product="ABI-L1b-Rad", domain='F')

A complete listing of the products available are available here.

There are methods to do the following:

   # Produce a pandas DataFrame of the available files in a time range
   df = G.df(start='2022-07-04 01:00', end='2022-07-04 01:30')

   # Download and read the data as an xarray Dataset nearest a specific time
   ds = G.nearesttime('2022-01-01')

   # Download and read the latest data as an xarray Dataset
   ds = G.latest()

   # Download data for a specified time range
   G.timerange(start='2022-06-01 00:00', end='2022-06-01 01:00')

   # Download recent data for a specific interval
   G.timerange(recent='30min')

RGB Recipes

The rgb xarray accessor computes various RGB products from a GOES ABI ABI-L2-MCMIP (multi-channel cloud and moisture imagry products) xarray.Dataset. See the demo for more examples of RGB products.

import matplotlib.pyplot as plt
ds = GOES().latest()
ax = plt.subplot(projection=ds.rgb.crs)
ax.imshow(ds.rgb.TrueColor(), **ds.rgb.imshow_kwargs)
ax.coastlines()

Field of View

The FOV xarray accessor creates shapely.Polygon objects for the ABI and GLM field of view. See notebooks for GLM and ABI field of view.

from goes2go.data import goes_latest
G = goes_latest()
# Get polygons of the full disk or ABI domain field of view.
G.FOV.full_disk
G.FOV.domain
# Get Cartopy coordinate reference system
G.FOV.crs

GOES-West is centered over -137 W and GOES-East is centered over -75 W. When GOES was being tested, it was in a "central" position, outlined in the dashed black line. Below is the ABI field of view for the full disk: field of view image

The GLM field of view is slightly smaller and limited by a bounding box. Below is the approximated GLM field of view: field of view image

How to Cite and Acknowledge

If GOES-2-go played an important role in your work, please tell me about it! Also, consider including a citation or acknowledgement in your article or product.

Suggested Citation

Blaylock, B. K. (2023). GOES-2-go: Download and display GOES-East and GOES-West data (Version 2022.07.15) [Computer software]. https://github.com/blaylockbk/goes2go

Suggested Acknowledgment

A portion of this work used code generously provided by Brian Blaylock's GOES-2-go python package (https://github.com/blaylockbk/goes2go)

What if I don't like the GOES-2-go or Python?

As an alternative you can use rclone to download GOES files from AWS. I quite like rclone. Here is a short rclone tutorial.

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I hope you find this makes GOES data easier to retrieve and display. Enjoy!

- Brian Blaylock

👨🏻‍💻 Contributing Guidelines
💬 GitHub Discussions
🚑 GitHub Issues
🌐 Personal Webpage

P.S. If you like GOES-2-go, check out my other python packages

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