Download GOES Data: A Guide to Accessing and Using Geostationary Satellite Images

GOES-R satellite instruments collect three times more data and provide four times better resolution of images of events taking place above Earth's surface... read more Resources

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GOES-R L2 data will be available on a rolling basis as products reach maturity. The L2 time series data are aggregated into daily and longer netCDF files, while the SUVI files are in FITS format. Real-time JSON files with partial data products for some GOES-R instruments are available from SWPC.

Mosaic images of the extended solar corona from GOES-17 SUVI special observing campaigns are available for August-September 2018, August-December 2019, and April 2021. GOES-18 data is now also available for July-September 2022. Access: GOES-17 Data, GOES-18 Data and the ReadMe.

2017-05-27 to 2017-05-28: A geomagnetic storm created various types of Ultra-low Frequency (ULF) waves in the magnetosphere. These waves were observed in space and on the ground, and the GOES-14 and GOES-16 magnetometer data are provided in order to compare to ground-based measurements and models of ULF waves during the event. Data were created as part of the Geospace Environment Modeling (GEM) ULF Wave Modeling, Effects, and Applications (UMEA) Challenge. See the ReadMe for caveats associated with these data.

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September 2017: The GOES-16 Solar Ultraviolet Imager (SUVI) captured images of a series of flares in Sept. 2017. At this time, the instrument was still in Beta status. NCEI reprocessed Level 0 data to create Level 1b images. These files are organized by SUVI wavelength, for instance Fe195 images. See the ReadMe for caveats associated with these data.

The GOES MAG data are reprocessed high-resolution versions of the operational GOES 8-15 MAG data that had not been previously made available at full resolution (2 Hz). The data were converted into GOES-R NetCDF and include daily files and plots.

One of the main features of Satpy is its ability to read various satellitedata formats. However, it currently only provides limited methods fordownloading data from remote sources and these methods are limited to demodata for Pytroll examples.See the examples and the demo API documentation for details.Otherwise, Satpy assumes all data is availablethrough the local system, either as a local directory or networkmounted file systems. Certain readers that use xarray to open data filesmay be able to load files from remote systems by using OpenDAP or similarprotocols.

The most common case of a remote system having access to data is with a cloudcomputing service like Google Cloud Platform (GCP) or Amazon WebServices (AWS). Another possible case is an organization having directbroadcast antennas where they receive data directly from the satellite orsatellite mission organization (NOAA, NASA, EUMETSAT, etc). In these casesdata is usually available as a mounted network file system and can be accessedlike a normal local path (with the added latency of network communications).

NOAA operates a constellation of Geostationary Operational Environmental Satellites to provide continuous weather imagery and monitoring of meteorological and space environment data for the protection of life and property across the United States. GOES satellites provide critical atmospheric, oceanic, climatic, and space weather products supporting weather forecasting and warnings, climatologic analysis and prediction, ecosystems management, safe and efficient transportation, and other national priorities.

The real-time feed and full historical archive of original resolution Advanced Baseline Imager (ABI) radiance data (Level 1b) and full resolution Cloud and Moisture Imager (CMI) products (Level 2) are freely available on Amazon S3 for anyone to use.

I would like to share with you a package that I developed in python to download and process data from GOES-16/17. This package aims to simplify the download of data from the amazon website, facilitate the navigation of ABI pixels, convert data from L1b to L2, crop images, to obtain information from many GLM files, calculate the density of flashes simply and quickly, among others. This package is available on my github: and on PyPi: A tutorial with many examples can be found at the following link: I hope the package will be of use to you. If you could share the package with your students and colleagues, I would be very grateful. Regards, Joao Huamán

For questions related to specific GOES Products, please visit the "[GOES-R website]( -r-series)" and navigate to the Help section.For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov

GOES-18 Interleave Testing - Has ended. GOES-West data now supplied from operational GOES-17 satellite. GOES-18 expected to become operational GOES-West in January 2023. See GOES-18 Interleave Testing for more information.

I have a app which downloads a huge amount of data (mostly images and document files) when it is installed for the first time. Currently, I'm only able to display the progress in a HUD. But I wish I could somehow allow the data to be downloaded when the app goes into background (or device gets locked). As the app is being targeted for devices running iOS 7.0 and above, I'm using NSURLSessionto download the data. I've gone through various threads here on Stackoverflow as well as a tutorial here. Even after making changes to my app as per the tutorial, my app does not continue the download. I tested it on an iPad. When the app is sent to background(or locked), the download is paused and resumes when the app comes to foreground.

Another approach could be to provide a button that the user clicks and data is downloaded while the user can still continue to use the device for other perposes. Any pointers as to how I can implement it?

4) If you want to check time remaining to download data in background, include following line in applicationDidEnterBackground:(UIApplication *)application delegate method of AppDelegate:

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This notebook shows how to make a true color image from the GOES-16Advanced Baseline Imager (ABI) level 2 data. We will plot the image withmatplotlib and Cartopy. The methods shown here are stitched together from thefollowing online resources:

The image above is not georeferenced. You can see the land and oceans, but wedo have enough information to draw state and country boundaries. Use themetpy.io package to obtain the projection information from the file. Thenuse Cartopy to plot the image on a map. The GOES data and image is on a[geostationary projection]( =geostationary).

I think the color looks a little dull. We could get complicated and make aRayleigh correction to the data to fix the blue light scattering, but that canbe intense. More simply, we can make the colors pop out by adjusting the imagecontrast. Adjusting image contrast is easy to do in Photoshop, and also easyto do in Python.

Scientists can now study the electrosphere over dimensions ranging from the Earth's radius all the way down to individual thunderstorms. Disseminating lightning information in near real time, on a continuous basis with other observable data, such as radar returns, cloud images, and other meteorological variables provides invaluable data to aid weather forecasters in detecting severe storms in time to give advance warning to the public.

In November 2017, GOES-16 (formerly GOES-R) was positioned in the GOES-EAST location centered over 75 degrees West longitude over the Western Hemisphere. This allows GLM to provide observation measurements between 52 degrees North and South latitude. GOES-16 instruments are now operating and data are available from NOAA CLASS (www.class.noaa.gov). GLM data are described at: GOES-R GLM Fact Sheet from NASA ( -r.gov/education/docs/Factsheet_GLM.pdf)

I would like to access the time-series dataset of X-Ray Flux obtained by GOES. I expect that the flux units to be Watts / square-meter; this is because of the figure below (obtained from this Nature paper).

I also tried accessing the flux dataset from NOAA SWPC; I clicked the "data" tab and click the "json" link, but this only contains data from the last week or so. I prefer to have the full dataset spanning a few decades. The same site hosts an FTP link; the text files present here have data fields for "year", "month", and "day", but not "hour", "minute", and "second".

I am hopeful that a representation of this dataset exists in the form of a text-file (such as this one provided by RHESSI). Regardless, I am not sure how to access this dataset. I wonder if the data I am seeking is in the netcdf files that I am unable to open, or if there is another way to do this - perhaps with astroquery or astropy?

If you would like the raw data in csv format, then you can get it from the official archive for GOES data. There you can find full an average data in csv format up to March 4th, 2020 (it stops then because GOES 16 and later are part of GOES-R). The link looks like this:

Double-click the ImageJ icon to launch the application and choose Help > Update > ImageJ.... A window will appear, telling you the version you are currently running and a list of upgrade versions. Choose the version you want to upgrade to (usually the most recent, or default version) and click OK. After the update downloads, you will need to re-launch ImageJ to run the new version.