NOAA Geostationary Operational Environmental Satellites (GOES) 16 & 17

Description

NOAA GOES-T will launch in March 2022!! For more information check out the GOES-T Webpage.

NEW GOES-T Data!!! Over the coming months, GOES-R Program plans to share data files externally once an instrument has reached Provisional maturity. Data files will be available between Provisional and the Operational Declaration of the satellite, however, data will have the caveat GOES-18 Preliminary, Non-Operational Data. The exception is during the interleave period when ABI Radiances and Cloud and Moisture Imagery data will be shared operationally via the Big Data Program.

GOES satellites (GOES-16 & GOES-17) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements.

Update Frequency

New data is added as soon as it's available

License

There are no restrictions on the use of this data.

Documentation

https://github.com/awslabs/open-data-docs/tree/main/docs/noaa/noaa-goes16

Managed By

See all datasets managed by NOAA.

Contact

For any questions regarding data delivery not associated with this platform or any general questions regarding the NOAA Big Data Program, email noaa.bdp@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 NOAA BDP team here: noaa.bdp@noaa.gov

How to Cite

NOAA Geostationary Operational Environmental Satellites (GOES) 16 & 17 was accessed on DATE from https://registry.opendata.aws/noaa-goes.

Usage Examples

Tutorials

• Comparison reading GOES-R data from AWS S3 in netCDF versus zarr by Chelle Gentemann
  
• Forecasting Hurricane Tracks with TensorFlow and data from AWS S3 by Kyle Archie
  
• NOAA GOES16 Julia Jupyter Notebook Example by Peter Schmiedeskamp
  CloudFormationEC2SageMaker
• Processing GOES-16 data with Dask & AWS Fargate by Zac Flamig
  CloudFormationFargateSageMaker
• Visualize GOES-16 in Python using Xarray by Hamed Alemohammad
  

Tools & Applications

• GOES-2-go is a python package for downloading and plotting plotting GOES data. by Brian K. Blaylock
  

Publications

• Beginner’s Guide to GOES-R Series Data by Danielle Losos
  
• Billions of Birds Migrate. Where Do They Go? by National Geographic
  
• Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations by Brian K. Blaylock, and John D. Horel
  
• Embracing the cloud for climate research by North Carolina State University’s North Carolina Institute for Climate Studies
  
• GOES Quick Guides (Spanish) by Anthony Segura García
  
• Imaging Considerations From a Geostationary Orbit Using the Short Wavelength Side of the Mid-Infrared Water Vapor Absorption Band by N.B. Miller, M.M. Gunshor, A.J. Merrelli, T.S. L'Ecuyer, T.J. Schmit, J.J. Gerth, N.J. Gordillo
  
• Observations of lightning in relation to transitions in volcanic activity during the 3 June 2018 Fuego Eruption by Christopher J. Schultz, Virginia P. Andrews, Kimberly D. Genareau, and Aaron R. Naeger
  
• Solar irradiance forecasting for the solar powered future by Solcast
  
• SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data by Dhileeban Kumaresan, Richard Wang, Ernesto Martinez, Richard Cziva, Alberto Todeschini, Colorado J Reed, Hossein Vahabi
  
• The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model by Shaowu Bao, Zhan Zhang, Evan Kalina, and Bin Liu