Applicability of ERA5 reanalysis of precipitation data in China
Received date: 2021-03-21
Revised date: 2021-07-07
Online published: 2022-01-21
ERA5 is the latest generation of ECMWF reanalysis product. Its precipitation data can be used to analyze the spatial and temporal characteristics of precipitation, extracting rainstorm and flood events, driving crop models, hydrological models, and land surface models. Studies on the effects of using reanalysis data to drive various models to simulate climate change are becoming more common in the context of climate change. However, as atmospheric reanalysis data, the data quality of ERA5 precipitation data will be affected by the errors of forecast products, observation data, and assimilation methods. The precision of reanalysis data will influence the uncertainty of related study results. As a result, verifying and evaluating the accuracy of reanalysis data is both necessary and urgent. To investigate the applicability of ERA5 precipitation data in China, daily precipitation data from 728 Chinese sites were used as a reference to examine the accuracy of ERA5 reanalysis precipitation data at various time scales (month, quarter), different climate zones and different elevation gradients using the Pearson correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), probability of detection (POD), false alarm rate, and equitable threat score. Furthermore, the capability of ERA5 to depict heavy rain and drought events was investigated. The results demonstrate that there are spatial and temporal differences in the ability of ERA5 precipitation data to identify daily precipitation events. Among the eight climatic zones, the north temperate zone has the highest accuracy. The r, RMSE, and MAE of ERA5 daily precipitation and station observation are 0.587, 4.040 mm·d-1, and 1.472 mm·d-1, respectively, in the north temperate zone. Precipitation data from the ERA5 are less accurate in the summer and autumn than in the winter and spring. The accuracy of ERA5 precipitation data in areas with elevations greater than >500 m is lower than in areas with elevations of less than ≤500 m. When the ERA5 data is used to identify the heavy rain, there is a large deviation from observations of the stations, and the larger the threshold (i.e., the stronger the heave rain), the larger the deviation is. The accuracy of standardized drought index (SPI) of different time scales calculated using ERA5 is different, varies with SPI on a three-month time scale having the highest accuracy. When using ERA5 data to identify drought events, the lower the threshold (that is, the greater the severity of the drought), the greater the error. Generally speaking, the drought capture ability of the north temperate zone, south temperate zone, and north subtropical zone is better. This research broadens the scope of validation of climate data sets and serves as a model for future research. These findings serve as a reference for related studies considering whether to use ERA5 data, as well as a tool for analyzing the uncertainties of related studies.
Key words: ERA5 data; precipitation; heavy rain; drought
Tingting LIU , Xiufang ZHU , Rui GUO , Kun XU , Shizhe ZHANG . Applicability of ERA5 reanalysis of precipitation data in China[J]. Arid Land Geography, 2022 , 45(1) : 66 -79 . DOI: 10.12118/j.issn.1000–6060.2021.132
[1] | 夏军, 谈戈. 全球变化与水文科学新的进展与挑战[J]. 资源科学, 2002, 24(3):1-7. |
[1] | [Xia Jun, Tan Ge. Hydrological science towards global change: Progress and challenge[J]. Resources Science, 2002, 24(3):1-7. ] |
[2] | Adler R F, Huffman G J, Chang A, et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present)[J]. Journal of hydrometeorology, 2003, 4(6):1147-1167. |
[3] | 谈戈, 夏军, 李新. 无资料地区水文预报研究的方法与出路[J]. 冰川冻土, 2004, 26(2):192-196. |
[3] | [Tan Ge, Xia Jun, Li Xin. Hydrological prediction in ungauged basins[J]. Journal of Glaciology and Geocryology, 2004, 26(2):192-196. ] |
[4] | Su F, Hong Y, Lettenmaier D P. Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin[J]. Journal of Hydrometeorology, 2008, 9(4):622-640. |
[5] | 黄建平, 张国龙, 于海鹏, 等. 黄河流域近40年气候变化的时空特征[J]. 水利学报, 2020, 51(9):1048-1058. |
[5] | [Huang Jianping, Zhang Guolong, Yu Haipeng, et al. Characteristics of climate change in the Yellow River Basin during recent 40 years[J]. Journal of Hydraulic Engineering, 2020, 51(9):1048-1058. ] |
[6] | 黄颖, 毛文茜, 王潇雅, 等. 近39 a祁连山及其周边地区降水量时空分布特征[J]. 干旱气象, 2020, 38(4):527-534. |
[6] | [Huang Ying, Mao Wenqian, Wang Xiaoya, et al. Temporal and spatial distribution of precipitation in the Qilian Mountain and its surrounding areas in recent 39 years[J]. Journal of Arid Meteorology, 2020, 38(4):527-534. ] |
[7] | 赵建婷, 王艳君, 苏布达, 等. 印度河流域气温、降水、蒸发及干旱变化特征[J]. 干旱区地理, 2020, 43(2):72-82. |
[7] | [Zhao Jianting, Wang Yanjun, Su Buda, et al. Spatiotemporal distributions of temperature, precipitation, evapotranspiration, and drought in the Indus River Basin[J]. Arid Land Geography, 2020, 43(2):72-82. ] |
[8] | 徐昆, 朱秀芳, 刘莹, 等. 采用AquaCrop作物生长模型研究中国玉米干旱脆弱性[J]. 农业工程学报, 2020, 36(1):154-161. |
[8] | [Xu Kun, Zhu Xiufang, Liu Ying, et al. Vulnerability of drought disaster of maize in China based on AquaCrop model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(1):154-161. ] |
[9] | 徐昆, 朱秀芳, 刘莹, 等. 气候变化下干旱对中国玉米产量的影响[J]. 农业工程学报, 2020, 36(11):149-158. |
[9] | [Xu Kun, Zhu Xiufang, Liu Ying, et al. Effects of drought on maize yield under climate change in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(11):149-158. ] |
[10] | 张小丽, 彭勇, 王本德, 等. 基于SWAT模型的降雨数据适用性评价[J]. 农业工程学报, 2014, 30(19):88-96. |
[10] | [Zhang Xiaoli, Peng Yong, Wang Bende, et al. Suitability evaluation of precipitation data using SWAT model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(19):88-96. ] |
[11] | 焦振航, 舒红, 吴凯, 等. 降水驱动数据改进对VIC土壤湿度模拟的影响[J]. 城市勘测, 2017(4):37-41. |
[11] | [Jiao Zhenhang, Shu Hong, Wu Kai, et al. The rainfall calibration methods’ impact on VIC soil moisture simulation[J]. Urban Surveying, 2017(4):37-41. ] |
[12] | Tarek M, Brissette F P, Arsenault R. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America[J]. Hydrology and Earth System Sciences, 2020, 24(5):2527-2544. |
[13] | 冯克鹏, 洪阳, 田军仓, 等. 多源降水数据的小流域水文模拟效用评估[J]. 干旱区地理, 2020, 43(5):1179-1191. |
[13] | [Feng Kepeng, Hong Yang, Tian Juncang, et al. Evaluating runoff simulation of multi-source precipitation data in small watersheds[J]. Arid Land Geography, 2020, 43(5):1179-1191. ] |
[14] | Albergel C, Dutra E, Munier S, et al. ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better?[J]. Hydrology and Earth System Sciences, 2018, 22(6):3515-3532. |
[15] | 韦芬芬, 汤剑平, 王淑瑜. 中国区域夏季再分析资料高空变量可信度的检验[J]. 地球物理学报, 2015, 58(2):383-397. |
[15] | [Wei Fenfen, Tang Jianping, Wang Shuyu. A reliability assessment of upper-level reanalysis datasets over China[J]. Chinese Journal of Geophysics, 2015, 58(2):383-397. ] |
[16] | 胡增运, 倪勇勇, 邵华, 等. CFSR, ERA-Interim和MERRA降水资料在中亚地区的适用性[J]. 干旱区地理, 2013, 36(4):700-708. |
[16] | [Hu Zengyun, Ni Yongyong, Shao Hua, et al. Applicability study of CFSR, ERA-Interim and MERRA precipitation estimates in Central Asia[J]. Arid Land Geography, 2013, 36(4):700-708. ] |
[17] | Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730):1999-2049. |
[18] | Graham R M, Hudson S R, Maturilli M. Improved performance of ERA5 in Arctic gateway relative to four global atmospheric reanalyses[J]. Geophysical Research Letters, 2019, 46(11):6138-6147. |
[19] | Hénin R, Liberato M L, Ramos A M, et al. Assessing the use of satellite-based estimates and high-resolution precipitation datasets for the study of extreme precipitation events over the Iberian Peninsula[J]. Water, 2018, 10(11):1688, doi: 10.3390/w10111688. |
[20] | Wang C, Graham R M, Wang K, et al. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: Effects on sea ice thermodynamics and evolution[J]. The Cryosphere, 2019, 13(6):1661-1679. |
[21] | Betts A K, Chan D Z, Desjardins R L. Near-surface biases in ERA5 over the Canadian prairies[J]. Frontiers in Environmental Science, 2019, 7:129, doi: 10.3389/fenvs.2019.00129. |
[22] | Beck H E, Pan M, Roy T, et al. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS[J]. Hydrology and Earth System Sciences, 2019, 23(1):207-224. |
[23] | Nogueira M. Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences[J]. Journal of Hydrology, 2020, 583:124632, doi: 10.1016/j.jhydrol.2020.124632. |
[24] | Xu X, Frey S K, Boluwade A, et al. Evaluation of variability among different precipitation products in the Northern Great Plains[J]. Journal of Hydrology: Regional Studies, 2019, 24:100608, doi: 10.1016/j.ejrh.2019.100608. |
[25] | Amjad M, Yilmaz M T, Yucel I, et al. Performance evaluation of satellite-and model-based precipitation products over varying climate and complex topography[J]. Journal of Hydrology, 2020, 584:124707, doi: 10.1016/j.jhydrol.2020.124632. |
[26] | Fallah A, Rakhshandehroo G R, Berg P, et al. Evaluation of precipitation datasets against local observations in southwestern Iran[J]. International Journal of Climatology, 2020, 40(9):4102-4116. |
[27] | Jiang Q, Li W, Fan Z, et al. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland[J]. Journal of Hydrology, 2020: 125660, doi: 10.1016/j.jhydrol.2020.125660. |
[28] | 成晓裕, 王艳华, 李国春, 等. 三套再分析降水资料在中国区域的对比评估[J]. 气候变化研究进展, 2013, 9(4):258-265. |
[28] | [Cheng Xiaoyu, Wang Yanhua, Li Guochun, et al. Evaluation of three reanalysis precipitation datasets in China[J]. Climate Change Research, 2013, 9(4):258-265. ] |
[29] | 孙葭, 章新平, 黄一民. 不同再分析降水数据在洞庭湖流域的精度评估[J]. 长江流域资源与环境, 2015, 24(11):1850-1859. |
[29] | [Sun Jia, Zhang Xinping, Huang Yimin. Evaluation of precipitation from ERA-Interim, CRU, GPCP and TRMM reanalysis data in the Dongting Lake Basin[J]. Resources and Environment in the Yangtze Basin, 2015, 24(11):1850-1859. ] |
[30] | 刘鹏飞, 刘丹丹, 梁丰, 等. 三套再分析降水资料在东北地区的适用性评价[J]. 水土保持研究, 2018, 25(4):215-221. |
[30] | [Liu Pengfei, Liu Dandan, Liang Feng, et al. Comparison the adaptability of CFSR, MERRA, NCEP reanalysis precipitation data and observation in northeast China[J]. Research of Soil and Water Conservation, 2018, 25(4):215-221. ] |
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