Characteristics of Reprocessed Hydrometeorological Automated Data System (HADS) Hourly Precipitation Data
Abstract
The Hydrometeorological Automated Data System (HADS) is a real-time data acquisition, processing, and distribution system operated by the Office of Hydrologic Development (OHD) of NOAA’s National Weather Service (NWS). The initial reprocessing of HADS data from its original format since its inception in July 1996 has been completed at NOAA’s National Climatic Data Center (NCDC). The quality of the reprocessed HADS hourly precipitation data from rain gauges is assessed by two objective metrics: the average fraction of missing values and the percentage of top-of-the-hour observations for a 3-yr period (2003–05). Pairwise comparisons between the reprocessed product and the real-time product are made using representative samples (about 13%) from the 48 contiguous United States. The monthly average of missing values varies from 0.5% to 2% in the reprocessed product and from 1.7% to 10.1% in the real-time product. Except for January 2003, the reprocessed product consistently reduced missing values, by as much as 9.4% in October 2004. The availability of top-of-the-hour observations is about 85% in the reprocessed product, while the real-time product has top-of-the-hour observations only about 50% of the time. This paper discusses real-time product quality issues, additional quality assurance algorithms used in the reprocessing environment, and the design of system-wide performance comparisons. Thus, the benefits to users of reprocessing the HADS data are the correction of 4-h observation time errors during 1 July–11 August 2005 and the demonstration of diurnals pattern of precipitation frequencies in regional domains. A Web-based interactive quality assessment tool for reprocessed HADS hourly precipitation data and access to the data are also presented.
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Current quality control of surface precipitation data in China primarily relies on precipitation-threshold-based traditional methods, such as regional extreme value checks, temporal consistency checks, and spatial consistency comparisons.
“Currently, the quality control (QC) of surface precipitation data in China primarily relies on precipitation-threshold-based traditional methods, such as regional extreme value checks, temporal consistency checks, and spatial consistency comparisons between stations [16][17][18][19].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [17]
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Quality control of a global hourly rainfall dataset
Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean, F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., et al. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105169 · Cited by 67 · see citing works
A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4 · see citing works
- Authors
- Kim, D., Nelson, B., & Seo, D.-J.
- Affiliations
- NOAA/NESDIS/NCDC, Asheville, North Carolina
- National Oceanic and Atmospheric Administration (US)
- Venue
- Weather and Forecasting
- Publisher
- American Meteorological Society
- Published
- 2009-10-01
- Volume
- 24
- Issue
- 5
- Pages
- 1287-1296
- ISSN
- 0882-8156 (ISSN-L)
- 1520-0434
- OpenAlex
- W2136845536
- Cited by
- 54
- References
- 0
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1 · 2022: 6 · 2021: 12 · 2020: 6 · 2019: 3 · 2018: 4 · 2017: 1 · 2016: 3 · 2015: 5 · 2014: 2 · 2013: 1 · 2012: 4
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Weather and Forecasting (publishedVersion)
- Funders
- U.S. Department of Agriculture
- U.S. Geological Survey
- U.S. Army Corps of Engineers
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Earth and Planetary Sciences › Geology › Environmental Monitoring and Data Management
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- Hospital Anxiety and Depression Scale, data reprocessing, rain gauge observations, data quality assessment, missing data, contiguous United States
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