Application of gauge-radar-satellite data in surface precipitation quality control
Precipitation observation data from different departments are highly complementary in the application, but there are some differences in observation equipment, data sampling methods, accuracy, and data transmission methods. To better apply the precipitation data of different departments in meteorolo...
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Veröffentlicht in: | Meteorology and atmospheric physics 2024-10, Vol.136 (5), p.33, Article 33 |
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Sprache: | eng |
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Zusammenfassung: | Precipitation observation data from different departments are highly complementary in the application, but there are some differences in observation equipment, data sampling methods, accuracy, and data transmission methods. To better apply the precipitation data of different departments in meteorological services, it is necessary to carry out a quality control method. In this study, rain gauge precipitation data, radar data, and satellite data from the China Meteorological Administration are used to perform collaborative quality control of precipitation data from the Ministry of Water Resources of the People’s Republic of China. The threshold value, spatial consistency, and temporal consistency are verified using meteorological station precipitation data, and the relationship thresholds between satellite and radar products and hourly precipitation are summarized and verified for consistency. Subsequently, collaborative quality control results are derived using a comprehensive scoring method. Testing this quality control method suggests that the method will not classify too many correct data as mistakes and the detection rate of incorrect data can be more than 0.7. Following quality control, the hourly precipitation error for hydrological station data fell dramatically, the False Alarm Rate decreased by 19%, and the anomalous maxima were successfully eliminated. Therefore, this collaborative quality control method can compensate for the deficiencies of a single quality-control source, thus allowing precipitation data not from the meteorological industry to be screened effectively. |
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ISSN: | 0177-7971 1436-5065 |
DOI: | 10.1007/s00703-024-01028-w |