ERA5再分析降水数据在中国的适用性分析

doi:10.12118/j.issn.1000–6060.2021.132

干旱区地理 ›› 2022, Vol. 45 ›› Issue (1): 66-79.doi: 10.12118/j.issn.1000–6060.2021.132

ERA5再分析降水数据在中国的适用性分析

刘婷婷^1,²(),朱秀芳^1,^2,³(),郭锐^1,²,徐昆^1,²,张世喆^1,²

1.北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
2.北京师范大学地理科学学部遥感科学与工程研究院,北京 100875
3.北京师范大学遥感科学国家重点实验室,北京 100875

收稿日期:2021-03-21 修回日期:2021-07-07 出版日期:2022-01-25 发布日期:2022-01-21
通讯作者: 朱秀芳
作者简介:刘婷婷（1998-）,女,在读硕士,主要从事极端气候研究. E-mail: 202021051185@mail.bnu.edu.cn
基金资助:
国家重点研发计划(2019YFAO606900);国家自然科学基金面上基金(42077436)

Applicability of ERA5 reanalysis of precipitation data in China

LIU Tingting^1,²(),ZHU Xiufang^1,^2,³(),GUO Rui^1,²,XU Kun^1,²,ZHANG Shizhe^1,²

1. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
2. Institute of Remote Sensing Science and Engineering, Faculty of Geographic Sciences, Beijing Normal University, Beijing 100875, China
3. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

Received:2021-03-21 Revised:2021-07-07 Online:2022-01-25 Published:2022-01-21
Contact: Xiufang ZHU

摘要/Abstract

摘要：

为了探讨ERA5再分析降水数据在中国区的适用性,以全国728个站点的日降水数据为参考,使用Pearson相关系数、均方根误差、平均绝对误差、探测率、误报率以及公正先兆评分分析了ERA5再分析降水数据在不同时间尺度（月、季）、不同气候区、不同海拔梯度下的精度以及ERA5再分析数据对暴雨和干旱事件的刻画能力。结果表明：ERA5降水数据对日降水事件的识别能力在空间和时间上均存在差异,整体来看：在北温带的精度最高;在夏、秋两季较冬、春两季的精度低;海拔>500 m地区的精度低于海拔≤500 m地区的精度。ERA5数据在进行暴雨识别时,与站点观测数据的偏差较大,且阈值越大（即暴雨越强）偏差越大;基于ERA5计算的不同尺度的标准化降水指数的精度不同,其中3个月尺度的标准化降水指数的精度最高。在进行干旱事件的识别时,阈值越低（即干旱越严重）误差越大。本研究可为ERA5降水数据的使用范围和使用方法提供参考,有助于分析相关研究的不确定性。

关键词: ERA5数据, 降水, 暴雨, 干旱

Abstract:

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

刘婷婷,朱秀芳,郭锐,徐昆,张世喆. ERA5再分析降水数据在中国的适用性分析[J]. 干旱区地理, 2022, 45(1): 66-79.

LIU Tingting,ZHU Xiufang,GUO Rui,XU Kun,ZHANG Shizhe. Applicability of ERA5 reanalysis of precipitation data in China[J]. Arid Land Geography, 2022, 45(1): 66-79.

图/表 12

图1

表1

用于评价ERA5数据的指标计算公式"

统计指标	计算公式	取值范围	最优值
Pearson相关系数（r）	$r = ∑ i = 1 N (E i - E ̅) (G i - G ̅) ∑ i = 1 N (E i - E ̅) 2 ∑ i = 1 N (G i - G ̅) 2$	[-1, 1]	1
均方根误差（RMSE）	$RMSE = ∑ i = 1 N (E i - G i) 2 N$	[0, +∞]	0
平均绝对误差（MAE）	$MAE = ∑ i = 1 N E i - G i N$	[0, +∞]	0
探测率（POD）	$POD = hit hit + miss$	[0, 1]	1
误报率（FAR）	$FAR = flasealarm hit + falsealarm$	[0, 1]	0
公正先兆评分（ETS）	$ETS = hit - He hit + miss + falsealarm - He He = (hit + falsealarm) (hit + miss) N$	[-1/3, 1]	1

表1

表2

图2

图3

图4

图5

图6

图7

图8

图9

表3

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类型	标准化降水指数（SPI）
无旱	SPI>-0.5
轻旱	-1.0<SPI≤-0.5
中旱	-1.5<SPI≤-1.0
重旱	-2.0<SPI≤-1.5
特旱	SPI≤-2.0

气候区	探测率（POD）	误报率（FAR）	公平先兆评分（ETS）
高原气候区	0.653	0.334	0.351
北温带	0.717	0.291	0.419
中温带	0.685	0.302	0.386
南温带	0.693	0.295	0.401
北亚热带	0.706	0.298	0.403
中亚热带	0.661	0.327	0.356
南亚热带	0.688	0.305	0.388
北热带	0.662	0.326	0.352

ERA5再分析降水数据在中国的适用性分析

Applicability of ERA5 reanalysis of precipitation data in China

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