
NOAA Analysis of Record for Calibration (AORC) Dataset
Provided by: NOAA , part of the Amazon Sustainability Data Initiative
Provided by: NOAA , part of the Amazon Sustainability Data Initiative

NOAA Analysis of Record for Calibration (AORC) Dataset
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 Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the native AORC process generates netCDF output, the data is post-processed to create a cloud optimized Zarr formatted equivalent for dissemination using cloud technology and infrastructure. AORC Version 1.1 dataset creation The AORC dataset was created after reviewing, identifying, and processing multiple large-scale, observation, and analysis datasets. There are two versions of The Analysis Of Record for Calibration (AORC) data. The initial AORC Version 1.0 dataset was completed in November 2019 and consisted of a grid with 8 elements at a resolution of 30 arc seconds. The AORC version 1.1 dataset was created to address issues "see Table 1 in Fall et al., 2023 " in the version 1.0 CONUS dataset. Full documentation on version 1.1 of the AORC data and the related journal publication are provided below. The native AORC version 1.1 process creates a dataset that consists of netCDF files with the following dimensions: 1 hour, 4201 latitude values (ranging from 25.0 to 53.0), and 8401 longitude values (ranging from -125.0 to -67). The data creation runs with a 10-day lag to ensure the inclusion of any corrections to the input Stage IV and NLDAS data. Note - The full extent of the AORC grid as defined in its data files exceed those cited above; those outermost rows and columns of data grids are filled with missing values and are the remnant of an early set of required AORC extents that have since been adjusted inward. AORC Version 1.1 Zarr Conversion The goal for converting the AORC data from netCDF to Zarr was to allow users to quickly and efficiently load/use the data. For example, one year of data takes 28 mins to load via NetCDF while only taking 3.2 seconds to load via Zarr (resulting in a substantial increase in speed). For longer periods of time, the percentage increase in speed using Zarr (vs NetCDF) is even higher. Using Zarr also leads to less memory and CPU utilization. It was determined that the optimal conversion for the data was 1 year worth of Zarr files with a chunk size of 18MB. The chunking was completed across all 8 variables. The chunks consist of the following dimensions: 144 time, 128 latitude, and 256 longitude. To create the files in the Zarr format, the NetCDF files were rechunked using chunk() and "Xarray ". After chunking the files, they were converted to a monthly Zarr file. Then, each monthly Zarr file was combined using "to_zarr " to create a Zarr file that represents a full year Users wanting more than 1 year of data will be able to utilize Zarr utilities/libraries to combine multiple years up to the span of the full data set. There are eight variables representing the meteorological conditions Total Precipitaion (APCP_surface) 1) Hourly total precipitation (kgm-2 or mm) for Calibration (AORC) dataset Air Temperature (TMP_2maboveground) 1) Temperature (at 2 m above-ground-level (AGL)) (K) Specific Humidity (SPFH_2maboveground) 1) Specific humidity (at 2 m AGL) (g g-1) Downward Long-Wave Radiation Flux (DLWRF_surface) 1) longwave (infrared) 2) radiation flux (at the surface) (W m-2) Downward Short-Wave Radiation Flux (DSWRF_surface) 1) Downward shortwave (solar) 2) radiation flux (at the surface) (W m-2) Pressure (PRES_surface) 1) Air pressure (at the surface) (Pa) U-Component of Wind (UGRD_10maboveground)" 1)U (west-east) - components of the wind (at 10 m AGL) (m s-1) V-Component of Wind (VGRD_10maboveground)" 1) V (south-north) - components of the wind (at 10 m AGL) (m s-1) Precipitation and Temperature The gridded AORC precipitation dataset contains one-hour Accumulated Surface Precipitation (APCP) ending at the “top” of each hour, in liquid water-equivalent units (kg m-2 to the nearest 0.1 kg m-2), while the gridded AORC temperature dataset is comprised of instantaneous, 2 m above-ground-level (AGL) temperatures at the top of each hour (in Kelvin, to the nearest 0.1). Specific Humidity, Pressure, Downward Radiation, Wind The development process for the six additional dataset components of the Conus AORC [i.e., specific humidity at 2m above ground (kg kg-1); downward longwave and shortwave radiation fluxes at the surface (W m-2); terrain-level pressure (Pa); and west-east and south-north wind components at 10 m above ground (m s-1)] has two distinct periods, based on datasets and methodology applied: 1979–2015 and 2016–present.
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 Analysis of Record for Calibration (AORC) Dataset was accessed on DATE
from https://registry.opendata.aws/noaa-nws-aorc .
Update frequency
To be determined
Support information
Managed by: NOAA
Contact: For questions regarding data content or quality, email the AORC team at aorc.info@noaa.gov. This data is made available to the public through the NOAA Open Data Dissemination (NODD) Program. For questions regarding this program, email 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 NOAA NODD team at NODD@NOAA.GOV.
General AWS Data Exchange support
Resources on AWS
Description
1-km resolution AORC version 1.1
Resource type
S3 Bucket
Amazon Resource Name (ARN)
arn:aws:s3:::noaa-nws-aorc-v1-1-1km
AWS Region
us-east-1
AWS CLI Access (No AWS account required)
aws s3 ls --no-sign-request s3://noaa-nws-aorc-v1-1-1km/
Explore
Usage examples
Tutorials
- Explore the AORC 1.1 dataset in Zarr by Michael AuCoin
Publications
- The Office of Water Prediction's Analysis of Record for Calibration, version 1.1: Dataset description and precipitation evaluation (09 July 2023). J. Am. Water Resour. Assoc., 59 (6). 1246-1272. by Greg Fall, David Kitzmiller, Sandra Pavlovic, Ziya Zhang, Nathan Patrick, Michael St. Laurent, Carl Trypaluk, Wanru Wu, and Dennis Miller