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ZHAN Yunjian, REN Guoyu, WANG Pengling, et al. 2021. Construction Method for Regionally Average Precipitation Time Series in China [J]. Climatic and Environmental Research (in Chinese), 26 (1): 45−57. doi: 10.3878/j.issn.1006-9585.2020.20012
Citation: ZHAN Yunjian, REN Guoyu, WANG Pengling, et al. 2021. Construction Method for Regionally Average Precipitation Time Series in China [J]. Climatic and Environmental Research (in Chinese), 26 (1): 45−57. doi: 10.3878/j.issn.1006-9585.2020.20012

Construction Method for Regionally Average Precipitation Time Series in China

  • In China, using different technical methods for studying the regional average precipitation characteristics based on station data could yield significantly different results. In this paper, with "China ground and CMORPH (CPC Morphing Technique) fusion daily precipitation product (V1.0)" as the reference value data, different methods for calculating the regional average precipitation time series in China were compared using the precipitation data of 2425 stations. These methods included the latitude and longitude grid area weighted average method of five grid sizes, the provincial area weighted average method, and the direct average method. The results show that the values of linear trend and the standard deviation of annual precipitation obtained using the 2.5° grid and 5.0° grid area average methods are the closest to those of the reference value. Therefore, 2.5° grid and 5.0° grid area average methods are optimal and suboptimal methods, respectively. The average annual precipitation for 15 years obtained using the provincial area weighted average method, which is commonly used in China’s climate monitoring business, is closest to the reference value. However, its estimation for linear trend and standard deviation is lower than the vale obtained from the 2.5° and 5.0° grid area weighted average methods. The sequence of percentage anomalies is inaccurate. If the grid is too sparse or too dense, the regional average result is far from the reference value. The error in the direct average method is also large, with low reliability.
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