This Readme.txt file was updated on April 14, 2021 as major changes have been made to the HRRR model output archive maintained by the MesoWest group at the University of Utah. This DOI available through the University of Utah Hive system refers to the metadata associated with the legacy HRRR model output archive maintained on the Pando object store of the Center for High Performance Computing (CHPC). More complete HRRR model output archives are maintained now and publicly accessible via Amazon Web Service Simple Storage Services and the Google Cloud Platform. Links to Publication Field checked. 2021-12-09, BP ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Metadata for High Resolution Rapid Refresh model output archive http://hrrr.chpc.utah.edu 2. Author Information Archive maintained by MesoWest group in the Department of Atmospheric Sciences Data hosted: UofU Center For High Performance Computing Pando Archive System Amazon Web Services (https://registry.opendata.aws/noaa-hrrr-pds/) Data source: NOAA Earth System Research Lab (https://rapidrefresh.noaa.gov/hrrr/) and NOAA Environmental Modeling Center (http://www.emc.ncep.noaa.gov/) Contact Information Name: John Horel Institution: Department of Atmospheric Science, University of Utah Email: john.horel@utah.edu Name: MesoWest Research Group Institution: Department of Atmospheric Science, University of Utah Email: atmos-mesowest@lists.utah.edu 3. Date of data collection HRRR model output is accessible via AWS and Google Cloud from 20140930 - present 4. Geographic location of data collection: Modeled atmospheric analyses and forecasts for Contiguous United States Alaska 5. Information about funding sources that supported the collection of the data: https://rapidrefresh.noaa.gov/hrrr/ -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: None; Creative Commons Attribution 2. Links to publications that cite or use the data: Blaylock, B. K., J. D. Horel, S. T. Liston, 2017: Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output. Computers and Geosciences. 109, 43-50. doi: https://doi.org/10.1016/j.cageo.2017.08.005 Blaylock, B., J. Horel, C. Galli, 2018: High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildfire Weather Assessment. Journal of Atmospheric and Oceanic Technology, 35, 2213-2227. https://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-18-0073.1 Blaylock, B., J. Horel, 2020: Comparison of Lightning Forecasts from the High-Resolution Rapid Refresh Model to Geostationary Lightning Mapper Observations. Wea. Forecasting. 35, 401-416. https://journals.ametsoc.org/doi/abs/10.1175/WAF-D-19-0141.1 Gowan, T., and J. Horel, 2020: Evaluation of IMERG-E Precipitation Estimates for Fire Weather Applications in Alaska. Wea. Forecasting, 35, 1831–1843. https://doi.org/10.1175/WAF-D-20-0023.1 Dougherty, K. J., J. D. Horel, J. E. Nachamkin, 2021: Forecast Skill for California Atmospheric River Events from the High-Resolution Rapid Refresh Model and the Coupled Ocean-Atmospheric Mesoscale Prediction System. Submitted to Weather and Forecasting. 3. Links to other publicly accessible locations of the data: HRRR UofU : http://hrrr.chpc.utah.edu HRRR EMC : http://www.nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/ HRRR ESRL : https://rapidrefresh.noaa.gov/hrrr/ HRRR Amazon Web Services: https://registry.opendata.aws/noaa-hrrr-pds/ HRRR Google: https://console.cloud.google.com/marketplace/product/noaa-public/hrrr 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? No 6. Recommended citation for the data: Blaylock, B. K., J. D. Horel, S. T. Liston, 2017: Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output. Computers and Geosciences. 109, 43-50. doi: https://doi.org/10.1016/j.cageo.2017.08.005 --------------------- DATA & FILE OVERVIEW --------------------- Base file format: GRIB2 GRIB2 is the most common format used for gridded output from operational numerical weather prediction models. HRRR GRIB2 files contain hundreds of two-dimensional fields each of which contain values at 1.9 million grid points. Info on reading GRIB2 files with wgrib2: http://www.cpc.ncep.noaa.gov/products/wesley/wgrib2/ It is recommended now to download GRIB2 files from (not from the Pando archive at the University of Utah): AWS: https://registry.opendata.aws/noaa-hrrr-pds/ Google: https://console.cloud.google.com/marketplace/product/noaa-public/hrrr Alternative file format: Zarr Zarr is a relatively new file format applicable to diverse use cases focussed on specific variables, regions, or times by breaking up the large 2-D GRIB2 files into 96 chunks for easier access Info on reading Zarr format: https://mesowest.utah.edu/html/hrrr/zarr_documentation/ The MesoWest group supports the creation and maintenance of the Zarr formatted data: AWS: https://registry.opendata.aws/noaa-hrrr-pds/ 2. Relationship between files: n/a 3. Additional related data collected that was not included in the current data package: n/a 4. Are there multiple versions of the dataset? New operational versions of the HRRR model have been implemented as follows: Version First Date v1 9/30/2014 V2 8/23/2016 V3 7/12/2018 V4 12/2/2020 HRRR V4 will be the last operational version of the HRRR model. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: HRRR files are downloaded continually from ESRL and EMC servers to the Google and AWS archives. 2. Methods for processing the data: n/a 3. Instrument- or software-specific information needed to interpret the data: GRIB2 format: wgrib2 (a command line tool for viewing grib2 data): http://www.cpc.noaa.gov/products/wesley/wgrib2/ pygrib (a python package for reading grib2 data): https://pypi.python.org/pypi/pygrib NOAA Climate Toolkit: https://www.ncdc.noaa.gov/wct/ Zarr format: https://mesowest.utah.edu/html/hrrr/zarr_documentation/ 4. Standards and calibration information, if appropriate: n/a 5. Environmental/experimental conditions: n/a 6. Describe any quality-assurance procedures performed on the data: n/a 7. People involved with sample collection, processing, analysis and/or submission: n/a ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: [FILENAME] ----------------------------------------- General information: http://hrrr.chpc.utah.edu/