NOAA High-Resolution Rapid Refresh (HRRR) Model
Provided by: NOAA , part of the Amazon Sustainability Data Initiative
Provided by: NOAA , part of the Amazon Sustainability Data Initiative
NOAA High-Resolution Rapid Refresh (HRRR) Model
Provided by: NOAA , part of the Amazon Sustainability Data Initiative
Provided by: NOAA , part of the Amazon Sustainability Data Initiative
This product is part of the Amazon Sustainability Data Initiative and contains data sets that are publicly available for anyone to access and use. No subscription is required. Unless specifically stated in the applicable data set documentation, data sets available through the Amazon Sustainability Data Initiative are not provided and maintained by AWS.
Description
The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh. The HRRR ZARR formatted data was originally generated by the University of Utah under a grant provided by NOAA. They are are continuing to publish ZARR versions of HRRR data. For information about data in the s3://hrrrzarr/ please contact atmos-mesowest@lists.utah.edu.
License
NOAA data disseminated through NODD is made available under the [Creative Commons 1.0 Universal Public Domain Dedication (CC0-1.0) license](https://creativecommons.org/publicdomain/zero/1.0/?ref=chooser-v1\ ), which is well-known and internationally recognized. There are no restrictions on the use of the data. The data are open to the public and can be used as desired. NOAA has adopted the Creative Commons license to ensure maximum use of our data, to spur and encourage exploration and innovation throughout the industry. This license is applicable to each of the NOAA datasets made available by NODD. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data.
How to cite
NOAA High-Resolution Rapid Refresh (HRRR) Model was accessed on DATE from https://registry.opendata.aws/noaa-hrrr-pds .
Update frequency
Hourly
Support information
Managed by: NOAA
Contact: 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
General AWS Data Exchange support
Resources on AWS
Description
Archive of HRRR data since 2014.
Resource type
S3 Bucket
Amazon Resource Name (ARN)
arn:aws:s3:::noaa-hrrr-bdp-pdsAWS Region
us-east-1AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://noaa-hrrr-bdp-pds/Explore
Description
New data notifications
Resource type
SNS Topic
Amazon Resource Name (ARN)
arn:aws:sns:us-east-1:123901341784:NewHRRRObjectAWS Region
us-east-1Description
HRRR Zarr format near-real time data archive managed by the University of Utah
Resource type
S3 Bucket
Amazon Resource Name (ARN)
arn:aws:s3:::hrrrzarrAWS Region
us-west-1AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://hrrrzarr/Explore
Usage examples
Tutorials
- HRRR-B Python package: download and read HRRR grib2 files by Brian Blaylock
- The HRRR Zarr Archive Managed by MesoWest by Taylor Gowan
Publications
- Using Cloud Computing to Analyze Model Output Archived in Zarr Format by Taylor A. Gowan, John D. Horel, Alexander A. Jacques, and Adair Kovac
- Using the U.S. Climate Reference Network to Identify Biases in Near- and Sub-Surface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model by Temple R. Lee, Ronald D. Leeper, Tim Wilson, Howard Diamond, Tilden P. Meyers, and David D. Turner