卫星降水产品对昆仑山北坡极端暴雨的监测能力及偏差分析

doi:10.13866/j.azr.2025.10.03

Abstract

Abstract:

The northern slope of the Kunlun Mountains is characterized by complex topography, extreme rainstorms, and an uneven distribution of meteorological stations, making it challenging to accurately capture the fine-scale spatial distribution and temporal evolution of extreme rainstorms using conventional, ground-based observations. Satellite precipitation products for monitoring rainstorms not only effectively fill the regional gap but also improve the capability of monitoring and early warning for severe catastrophic weather. On the basis of hourly precipitation data from 383 meteorological stations and eight sets of satellite precipitation products over the northern slope of the Kunlun Mountains, we selected three representative extreme rainstorm processes to comprehensively assess the applicability of eight satellite precipitation products for monitoring extreme precipitation events and quantitatively analyze their performance on the northern slope of the Kunlun Mountains. The results were as follows: (1) The GPM IMERG Early, GPM IMERG Late, and GPM IMERG Final products showed good performance in capturing the spatial distribution and magnitude of extreme precipitation, with hit rates exceeding 99%. Among them, the GPM IMERG Final product had the best performance, with a correlation coefficient of 0.64 between ground-observed precipitation and the GPM IMERG Final product during Event 3. The FY2H and FY4A precipitation products showed certain discrepancies in comparison with ground observations in both qualitative and quantitative evaluations, with low correlation coefficients (below 0.01). (2) In terms of temporal evolution characteristics of extreme precipitation processes, all satellite precipitation products showed certain biases, ranging from -95% to 250%. In comparison with the other satellite products, the GPM IMERG Final product outperformed in terms of precipitation amounts at each time step and temporal trends, with accuracy and threat score values above 0.8. (3) The spatial extent of the three extreme rainstorms reproduced by eight satellite precipitation products was smaller than that indicated by observations, and the intensity was weaker, with obvious underestimation characteristics. The missing detection bias generally accounted for more than 50% of the relative contribution rate of errors, which is one of the main reasons for the underestimation of extreme heavy precipitation events by satellite precipitation products. Overall, satellite precipitation products reflected the extreme precipitation processes on the northern slope of the Kunlun Mountains to a certain extent, but their accuracy in monitoring requires further improvement. These results provide a scientific basis for selecting appropriate satellite precipitation products for regional applications and offer data support for bias correction, algorithm optimization, and improved monitoring of extreme precipitation in this region.

Key words: northern slope of the Kunlun Mountains, extreme rainstorms, satellite precipitation products, monitoring, evaluation

YU Zhixiang, YANG Xia, YU Xiaojing, JIANG Xutao. Capability and biases of satellite precipitation products in monitoring extreme rainstorms along the northern slope of the Kunlun Mountains[J].Arid Zone Research, 2025, 42(10): 1777-1790.

Figures/Tables 12

Fig. 1

Tab. 1

Tab. 2

Summary of statistical evaluation methods used in this study"

统计指标	公式	最优值	物理意义
相关系数CC	$C C = ∑ i = 1 n (x i - x -) (y i - y -) ∑ i = 1 n (x i - x -) 2 ∑ i = 1 n (y i - y -) 2$	1	衡量卫星与观测降水量之间的线性关系强度和方向
平均绝对误差MAE	$M A E = ∑ i = 1 n y i - x i n$	0	表示卫星与观测降水量之间的差距大小
均方根误差RMSE	$R M S E = ∑ i = 1 n (y i - x i) 2 n$	0	衡量卫星与观测降水量之间的偏差程度
标准偏差比SDV	$S D V = ∑ i = 1 n (y i - y -) 2 n ∑ i = 1 n (x i - x -) 2 n$	1	表示卫星与观测降水量的变化幅度
相对误差Bias	$B i a s = ∑ i = 1 n y i - ∑ i = 1 n x i ∑ i = 1 n x i × 100 %$	0	表示卫星与观测降水量之间的系统性偏差
命中率POD	$P O D = N A N A + N C$	1	N_A为站点显示有雨，卫星数据也显示有雨；N_B为站点显示无雨，卫星数据显示有雨；N_C为站点显示有雨，卫星数据显示无雨；N_D为站点显示无雨，卫星数据也显示无雨
误报率FAR	$F A R = N B N A + N B$	0
TS评分	$T S = N A N A + N B + N C$	1
准确率AC	$A C = N A + N D N A + N B + N C + N D$	1

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Tab. 4

Tab. 5

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卫星降水产品	时间分辨率	空间分辨率	数据来源	关键信息
GPM IMERG Early	30 min	0.1°	NASA（https://www.earthdata.nasa.gov/）	Early采用云移动矢量传播算法中的前向传播算法，Late增加了后向传播算法，Final进行了地面雨量数据校准
GPM IMERG Final	30 min	0.1°
GPM IMERG Late	30 min	0.1°
GSMaP_NOW	30 min	0.1°	JAXA（https://sharaku.eorc.jaxa.jp/GSMaP/index.htm）	基于低轨卫星的微波数据和地球静止卫星的红外数据开发
GSMaP_NRT	1 h	0.1°
GSMaP_MVK	1 h	0.1°
FY2H	1 h	10 km	NSMC（http://satellite.nsmc.org.cn/portalsite/default.aspx）	采用卫星AGRI红外通道资料实时估计地面降水强度，未进行地面雨量计订正
FY4A	5 min	4 km	NSMC（http://satellite.nsmc.org.cn/PortalSite/Default.aspx）	采用卫星AGRI红外通道资料实时估计地面降水强度，未进行地面雨量计订正

过程	起止时间	总降水站数/个	暴雨站数/个	暴雨中心站点	暴雨中心累积降水量 /mm	最大1 h 降水量 /mm	最大1 h 降水量出现时次
1	2019年6月24日 20:00—25日20:00	286	29	和田地区策勒县奴尔乡努尔河战斗渠渠首站	61.3	23.5	25日9:00
2	2020年5月5日 20:00—6日20:00	270	11	巴州且末县奥依拉克镇莫勒切河水库	39.7	19.9	6日20:00
3	2021年6月15日 20:00—16日20:00	336	152	和田地区洛浦县山普鲁乡泥石流频发区1号站	106.6	28.8	15日21:00

遥感降水产品	相关系数	平均误差/mm	均方根误差/mm	相对偏差/%	命中率	误报率	关键成功指数
Early	0.24	-15.32	25.10	-60.42	0.16	0.44	0.14
Final	0.39	-7.17	18.02	-28.22	0.54	0.18	0.48
Late	0.22	-9.85	26.22	-41.61	0.27	0.45	0.22
MVK	0.41	-20.17	27.68	-77.51	0.10	0.47	0.09
NOW	0.46	-0.76	27.67	-4.49	0.25	0.76	0.14
NRT	0.39	-21.09	28.55	-81.12	0.08	0.52	0.07
FY2H	0.48	-20.25	28.91	-82.23	0.02	0.87	0.02
FY4A	0.11	-27.76	31.71	-99.69	0.00	-	0.00

遥感降水产品	相对偏差/%	偏差相对贡献率/%
遥感降水产品	相对偏差/%	命中	漏报	误报
Early	-60.42	-1.19	-66.06	6.83
Final	-28.22	-10.69	-24.65	7.12
Late	-41.61	-0.16	-56.55	15.10
MVK	-77.51	-3.70	-77.29	3.48
NOW	-4.49	10.72	-71.43	56.22
NRT	-81.12	-1.82	-83.31	4.01
FY2H	-82.23	0.10	-90.75	8.42
FY4A	-99.69	0.00	-99.69	0.00

Capability and biases of satellite precipitation products in monitoring extreme rainstorms along the northern slope of the Kunlun Mountains

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