Preliminary Study on Quality Control Method for Hydrological Station Precipitation Data in Haidong City, Qinghai Province

Haowei Yang; Bingyu Zhao; Weifu Zhang; Jie Yang; Chengyu Zhang

doi:10.54097/95n8xh72

Authors

Haowei Yang Haidong Meteorological Bureau, Haidong 810600, China
Bingyu Zhao Qinghai Meteorological Information Center, Xining 810001, China
Weifu Zhang Haidong Meteorological Bureau, Haidong 810600, China
Jie Yang Haidong Meteorological Bureau, Haidong 810600, China
Chengyu Zhang Haidong Meteorological Bureau, Haidong 810600, China

DOI:

https://doi.org/10.54097/95n8xh72

Keywords:

Collaborative quality control, hydrological station, precipitation data, weather radar, satellite infrared brightness temperature, Haidong City

Abstract

The accuracy of precipitation observations from hydrological stations is critical for multi-source fusion-based nowcasting products, particularly in the complex terrain of the Qinghai-Tibet Plateau. However, precipitation data from hydrological stations, which are managed by external departments, often suffer from inconsistent observation standards and variable data quality, limiting their direct applicability in operational meteorological services. This study proposes a collaborative quality control (QC) framework specifically designed for hourly precipitation data from hydrological stations in Haidong City, Qinghai Province. The methodology integrates three independent QC modules: (1) a climatological and spatiotemporal consistency check using adjacent meteorological stations, (2) a radar-based quantitative precipitation estimation (QPE) validation with dynamic rainfall-rate-dependent thresholds, and (3) a synergistic verification combining FY-4B geostationary satellite infrared brightness temperature (TBB) and weather radar composite reflectivity data. A weighted comprehensive scoring system based on grey relational analysis is then applied to derive final QC flags (correct, suspect, or erroneous). The proposed method was evaluated using data from a typical precipitation event on July 10, 2023, across 42 hydrological stations in Haidong. Results indicate that the collaborative approach effectively identifies anomalous observations, with the radar-reflectivity contradiction model capturing precipitation–reflectivity mismatches, and the satellite–radar synergy successfully flagging cases where zero precipitation coincides with deep convective clouds (TBB < 220 K) or measurable rainfall occurs under warm-cloud conditions (TBB > 265 K). The comprehensive scoring system reduced the proportion of erroneous stations from 4.8% to 0.7% after QC. This study demonstrates that multi-source collaborative QC significantly enhances the reliability of hydrological station precipitation data, providing a practical and transferable solution for improving multi-source fusion products in data-sparse plateau regions.

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