Bias Adjustment of Long‐Term (1961–2020) Daily Precipitation for China

Author:

Zhao Yanni12ORCID,Chen Rensheng1ORCID,Yang Zhiwei12ORCID,Liu Yiwen12,Zhao Linlin12,Yang Yong1ORCID,Wang Lei3

Affiliation:

1. Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou China

2. University of Chinese Academy of Sciences Beijing China

3. College of Geography and Environment Shandong Normal University Jinan China

Abstract

AbstractThe observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR‐measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind‐induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error‐adjustment amounts for wind‐induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high‐adjusted‐percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error‐adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai‐Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China.

Publisher

American Geophysical Union (AGU)

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