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

摘要/Abstract

摘要：

昆仑山北坡地形复杂，极端暴雨事件频发，但现有地面观测站点稀少且分布不均，难以捕捉复杂地形下极端暴雨的精细化分布和演变过程，高分辨率卫星数据为极端暴雨的监测提供了新的手段。本文基于昆仑山北坡383个气象台站逐小时降水数据和8种卫星降水产品，选取研究区内3次极端暴雨过程，综合评估了8种卫星降水产品在监测昆仑山北坡极端降水事件中的适用性，定量分析卫星降水产品对昆仑山北坡极端降水的监测能力。结果表明：（1） GPM IMERG Early、GPM IMERG Late、GPM IMERG Final降水产品在捕捉昆仑山北坡极端暴雨空间分布和降水量方面表现较好，命中率均大于99%。GPM IMERG Final产品表现最优，个例3中其与地面观测降水量的相关系数达0.64，FY2H和FY4A降水产品在极端降水过程定性和定量评估中均与站点实测有一定差距，相关系数最低在0.01以下。（2）在极端降水过程的时间演变特征方面，8种卫星降水产品均存在一定误差，偏差幅度在-95%~250%，相较于其他卫星产品GPM IMERG Final在各时次降水量和变化趋势上表现最优，准确率和TS评分超过0.8。（3） 8种卫星降水产品再现3次极端暴雨空间范围较观测偏小，在降水强度上整体偏弱，存在明显的低估特征。漏报误差在偏差相对贡献率中普遍占比在50%以上，这是导致卫星降水产品对强降水事件低估的主要原因之一。总体来看，卫星降水产品能在一定程度上反映昆仑山北坡极端降水过程，但其监测精度需进一步提升，该研究结果可以为研究区不同应用选用最合适的卫星降水产品提供科学依据，同时为降水产品偏差校正、算法改进及该区域极端降水的监测提供一定的数据支撑。

关键词: 昆仑山北坡, 极端暴雨, 卫星降水产品, 监测, 评估

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

于志翔, 杨霞, 于晓晶, 姜旭涛. 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[J]. 干旱区研究, 2025, 42(10): 1777-1790.

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.

图/表 12

图1

表1

表2

本文所用的统计评估方法"

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

表2

表3

图2

图3

图4

图5

图6

图7

表4

表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