GOES-DL

Since 1975, Geostationary Operational Environmental Satellites (GOES) have provided continuous imagery and data on atmospheric conditions and solar activity (space weather). They have even aided in search and rescue of people in distress. GOES data products have led to more accurate and timely weather forecasts and better understanding of long-term climate conditions. The National Aeronautics and Space Administration (NASA) builds and launches the GOES, and the National Oceanic and Atmospheric Administration (NOAA) operates them [6].

GOES-DL is an open-source Python package that simplifies the process of downloading satellite imagery datasets from various NOAA archives. The package supports both second and third-generation GOES satellite data [4,7], as well as the Gridded Satellite B1 (GridSat-B1) Climate Data Record [3]. GOES-DL provides an easy-to-use interface to access data for scientific analysis, research, and other applications.

Key Features

• Real-time GOES 3rd Generation Satellite Data (GOES Series R): Access real-time and archived data from NOAA's Amazon Web Services (AWS) cloud archive.

• Gridded Satellite Data from GOES 2nd Generation (GridSat-GOES): Download data from NOAA's National Centers for Environmental Information (NCEI) archive.

• Gridded Satellite Data from ISCCP B1 (GridSat-B1): Fetch historical climate data from both NOAA's AWS archive and the NCEI archive.

• Seamless integration of different data sources into a unified download process.

• High-level API that abstracts away the complexity of data access from NOAA archives.

Supported Datasets

1. GOES 2nd Generation (GOES-8 to GOES-15): Also known as the I to P Series, these datasets provide environmental monitoring and meteorological data for the Western Hemisphere [4].

2. GOES 3rd Generation (GOES-16 to GOES-18): Also known as the R to U Series, these satellites offer advanced imagery and atmospheric measurements with better spatial, spectral, and temporal resolution [7].

3. GridSat-B1 Climate Data Record (Version 2): Gridded satellite imagery for climate research, containing global infrared window, visible, and water vapor data over long time periods [3].

Refer to Gridded Satellite GOES (GridSat-GOES) East and West Full Disk and CONUS Coverage, Version 1 and NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature, Version 2 for more information on the data format and details of the content.

See NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17 & 18 and NOAA GOES on AWS (CICS) for information on the GOES-R Series data available from NOAA on AWS. You can find much more detailed information about GOES-R Series data from NOAA's Geostationary Operational Environmental Satellites - R Series.

Installation

To install GOES-DL, use pip:

pip install goes-dl

Usage

Below are examples of how to use the GOES-DL package to download data from each of the supported sources.

1. Download GOES 2nd Generation Data (from NOAA's NCEI archive)

# Import the locator and datasource according to your desired product
from GOES_DL.dataset.gridsat import GridSatProductLocatorGC
from GOES_DL.datasource import DatasourceHTTP
from GOES_DL.downloader import Downloader

# Initialize the downloader for GridSat-GOES (GOES-12, Full Disk)
locator = GridSatProductLocatorGC("F", "G12")

datasource = DatasourceHTTP(locator, repository="./my_data/gridsat-gc")

downloader = Downloader(
    datasource=datasource,
    locator=locator,
    date_format="%Y-%m-%dT%H:%M%z",  # use a custom short date format
)

# Set your desired date...
files1 = downloader.get_files(start="2012-08-23T00:00Z")

# ...or your desired date range
files2 = downloader.get_files(
   start="2012-08-23T00:00-0004",
   end="2012-08-24T00:00-0004",
)

# `files1` and files2` are lists of tuple[str, bytes] with file path and
# file content, respectively. The file path is relative to the base URL.

2. Download GOES 3rd Generation Data (from NOAA's AWS archive)

# Import the locator and datasource according to your desired product
from GOES_DL.dataset.goes import GOESProductLocatorABIPP
from GOES_DL.datasource import DatasourceAWS
from GOES_DL.downloader import Downloader

# Initialize the downloader for GOES-R Series (set your desired product)
locator = GOESProductLocatorABIPP("CMIP", "F", ["C02", "C08", "C13"], "G16")

# GOES-16 data is updated every 10 minutes. If you are downloading
# old data, you may leave the cache refresh rate as default (+inf).
datasource = DatasourceAWS(locator, repository="./my_data/goes-r", cache=600)

downloader = Downloader(
    datasource=datasource,
    locator=locator,
)

# Set your desired date...
files1 = downloader.get_files(start="2024-08-23T00:00:00Z")

# ...or your desired date range
files2 = downloader.get_files(
   start="2024-08-23T00:00:00-0004",  # use the default date format
   end="2024-08-24T00:00:00-0004",
)

# `files1` and files2` are lists of tuple[str, bytes] with file path and
# file content, respectively. The file path is relative to the base URL.

3. Download GridSat-B1 Data (from NOAA's AWS archive)

# Import the locator and datasource according to your desired product
from GOES_DL.dataset.gridsat import GridSatProductLocatorB1
from GOES_DL.datasource import DatasourceAWS
from GOES_DL.downloader import Downloader

# Initialize the downloader for GridSat-B1
locator = GridSatProductLocatorB1()

# Also available in HTTP from NCEI's archive, see next example
datasource = DatasourceAWS(locator, repository="./my_data/gridsat-b1")

downloader = Downloader(
    datasource=datasource,
    locator=locator,
    date_format="%Y-%m-%dT%H:%M%z",
)

# Set your desired date...
files1 = downloader.get_files(start="1984-08-23T00:00Z")

# ...or your desired date range
files2 = downloader.get_files(
   start="1984-08-23T00:00-0004",
   end="1984-08-24T00:00-0004",
)

# `files1` and files2` are lists of tuple[str, bytes] with file path and
# file content, respectively. The file path is relative to the base URL.

4. Download GridSat-B1 Data (from NOAA's NCEI archive)

# Import the locator and datasource according to your desired product
from GOES_DL.dataset.gridsat import GridSatProductLocatorB1
from GOES_DL.datasource import DatasourceHTTP
from GOES_DL.downloader import Downloader

# Initialize the downloader for GridSat-B1
locator = GridSatProductLocatorB1()

# NCEI archive has the same folder structure as AWS, so, if you have
# downloaded data from AWS, you can use the same locator and change the
# datasource to HTTP. If a file is not found in the local repository, it
# will be downloaded from the remote datasource. In all previous examples,
# if a file was already downloaded, it will not be downloaded again.
datasource = DatasourceHTTP(locator, repository="./my_data/gridsat-b1")

downloader = Downloader(
    datasource=datasource,
    locator=locator,
    date_format="%Y-%m-%dT%H:%M%z",
)

# Set your desired date...
files1 = downloader.get_files(start="1984-08-23T00:00Z")

# ...or your desired date range
files2 = downloader.get_files(
   start="1984-08-23T00:00-0004",
   end="1984-08-24T00:00-0004",
)

# `files1` and files2` are lists of tuple[str, bytes] with file path and
# file content, respectively. The file path is relative to the base URL.

Pipeline and parameters

The general workflow for downloading data using GOES-DL is as follows:

1. Initialize the locator: Import the appropriate locator class for the desired product and satellite and initialize a locator object. The product locator provides the necessary information to find the data files in the dataset repository. This is the only step that is specific to the dataset being downloaded.
2. Initialize the datasource: Import the appropriate datasource class for the desired dataset and instantiate a datasource object. The datasource provides the necessary functionality to access the data files from the repository, it abstracts the complexity of accessing data from different sources. This step is common to all datasets. The datasource is Initialized with the locator object.
3. Initialize the downloader: Import the downloader class and initialize a downloader object. The downloader is the main interface for downloading data. It is initialized with the datasource and locator objects, as well as the date format to be used in the download process.

The Downloader.get_files method accepts the following parameters:

• start_time: A string specifying the starting date for the dataset to be downloaded.
• end_time: A string specifying the ending date for the dataset to be downloaded. If not provided, only the data for the start_time will be downloaded.

The default date format is the ISO 8601 format with timezone information ("%Y-%m-%dT%H:%M%z"). The date format can be changed by passing the desired format to the downloader object during initialization.

Data Sources

1. NOAA NCEI Archive: GridSat-B1 Climate Data Record and GOES-8 to GOES-15 data is available through NOAA’s National Centers for Environmental Information.
2. NOAA AWS Cloud Archive: GOES-16 to GOES-18 data and GridSat-B1 Climate Data Record are accessible via the NOAA archive hosted on AWS.

Contributing

Contributions to GOES-DL are welcome! If you'd like to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Open a pull request with a description of your changes.

Please make sure to include tests for any new functionality.

Requirements

• Python 3.8+
• requests: A simple, yet elegant, HTTP library for Python.
• boto3: AWS SDK for Python.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This package relies on data provided by NOAA’s NCEI and NOAA’s archive on AWS.

Credits

When using GOES-DL in any research, publication or website, please cite this package as:

Villamayor-Venialbo, W. (2024): GOES-DL: A Python package for downloading GOES and GridSat-B1 satellite data (Version 0.1.1) [Software]. GitHub. git:wvenialbo/GOES-DL, [indicate access date].

Credits for GridSat-GOES/CONUS

Dataset Citation:

Knapp, K. R. (2017): Gridded Satellite GOES Coverage Data (GridSat-GOES), [indicate subset used]. NOAA National Centers for Environmental Information doi:10.7289/V5HM56GM, [indicate access date].

Please cite the following article when using GridSat-GOES/CONUS data in any publication:

Knapp, K. R. and Wilkins, S. L.: Gridded Satellite (GridSat) GOES and CONUS data, Earth System Science Data, 10(3), 1417–1425, doi:10.5194/essd-10-1417-2018, 2018.

Credits for GridSat-B1

Dataset Citation:

Knapp, K. R.; NOAA CDR Program; (2014): NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature, Version 2, [indicate subset used]. NOAA National Centers for Environmental Information. doi:10.7289/V59P2ZKR, [indicate access date].

Please cite the following article when using GridSat-B1 data in any publication:

Knapp, K. R., Ansari S.; Bain, C. L.; Bourassa, M. A.; Dickinson, M. J.; Funk, C.; Helms, C. N.; Hennon, C. C.; Holmes, C. D.; Huffman, G. J.; Kossin, J. P.; Lee, H.-T.; Loew, A.; and Magnusdottir, G.: Globally gridded satellite (GridSat) observations for climate studies. Bulletin of the American Meteorological Society, 92(7), 893-907, doi:10.1175/2011BAMS3039.1, 2011.

When possible, please cite the following article when using the ISCCP-B1 data or other ISCCP-B1 imagery or GIBBS imagery in a publication or website:

Knapp, K. R.: Scientific data stewardship of International Satellite Cloud Climatology Project B1 global geostationary observations. Journal of Applied Remote Sensing, 2(1), 023548, doi:10.1117/1.3043461, 2008.

Contact and Support

For issues, questions, or requests, feel free to open an issue on this repository or contact the author, wvenialbo at gmail.com.

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Similar Projects

• Brian Blaylock's goes2go: Download and process GOES-16 and GOES-17 data from NOAA's archive on AWS using Python. (readthedocs)
• Joao Henry's GOES: Python package to download and manipulate GOES-16/17/18 data.

References

1. Knapp, K. R. (2008): Scientific data stewardship of International Satellite Cloud Climatology Project B1 global geostationary observations. Journal of Applied Remote Sensing, 2(1), 023548, doi:10.1117/1.3043461.
2. Knapp, K. R., Ansari S.; Bain, C. L.; Bourassa, M. A.; Dickinson, M. J.; Funk, C.; Helms, C. N.; Hennon, C. C.; Holmes, C. D.; Huffman, G. J.; Kossin, J. P.; Lee, H.-T.; Loew, A.; and Magnusdottir, G.; (2011): Globally gridded satellite (GridSat) observations for climate studies. Bulletin of the American Meteorological Society, 92(7), 893-907, doi:10.1175/2011BAMS3039.1.
3. Knapp, K. R; NOAA CDR Program; (2014): NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature, Version 2. NOAA National Centers for Environmental Information, doi:10.7289/V59P2ZKR.
4. Knapp, K. R; (2017): Gridded Satellite GOES Coverage Data (GridSat-GOES). NOAA National Centers for Environmental Information. doi:10.7289/V5HM56GM.
5. Knapp, K. R. and Wilkins, S. L.; (2018): Gridded Satellite (GridSat) GOES and CONUS data, Earth System Science Data, 10(3), 1417–1425, doi:10.5194/essd-10-1417-2018.
6. GOES History. GOES-R Website, https://www.goes-r.gov/mission/history.html, retrieved on 2024.
7. GOES-R Series Data Products. GOES-R Website, https://www.goes-r.gov/products/overview.html, retrieved on 2024.
8. NOAA Big Data Program, NOAA Open Data Dissemination Program, https://github.com/NOAA-Big-Data-Program/bdp-data-docs, retrieved on 2024.
9. Beginner’s Guide to GOES-R Series Data: How to acquire, analyze, and visualize GOES-R Series data, Resources compiled by GOES-R Product Readiness and Operations, Satellite Products and Services Division, National Oceanic and Atmospheric Administration. PDF Last Updated on May 23, 2024, retrieved on 2024.
10. GOES-R Series Data Book, GOES-R Series Program Office, Goddard Space Flight Center, National Aeronautics and Space Administration. PDF, retrieved on 2024.