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遥感技术与应用  2019, Vol. 34 Issue (5): 1121-1132    DOI: 10.11873/j.issn.1004-0323.2019.5.1121
降水遥感观测专栏     
GSMaP遥感降水产品对典型极端降水事件监测能力评估
高玥1(),徐慧1(),刘国2
1. 河海大学 水文水资源学院,江苏 南京 210098
2. 河海大学 地球科学与工程学院,江苏 南京 211100
Evaluation of the GSMaP Estimates on Monitoring Extreme Precipitation Events
Yue Gao1(),Hui Xu1(),Guo Liu2
1. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
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摘要:

针对2017年6月下旬至7月上旬期间湖南省出现的持续强降水过程,采用遥感降水误差标定体系和遥感降水误差分解模型方法,综合标定了GSMaP_NRT、GSMaP_MVK和GSMaP_Gauge等3套遥感降水产品的数据精度,具体分析了各数据产品总体误差的组成结构及各项独立误差成分(命中误差、漏测误差、误报误差)的变化特征,并重点讨论了3套遥感降水产品针对大雨及以上级别强降水事件的监测能力,结果表明:①3套降水产品都能基本捕捉到此次强降水过程的空间分布特征,也能够较为准确地描绘出不同强弱降水阶段的交替过程;②总体上看,GSMaP_Gauge产品表现最好,3套遥感降水产品在复杂地形地区内精度都相对更差。在误差组成结构中,命中误差占总误差比重最大;③对于大雨及以上级别的强降水事件,GSMaP_Gauge产品表现出最佳监测效果,相比两套未校正数据产品,GSMaP_Gauge产品对于漏测误差的改善效果十分显著,其能更有效地监测到强降水事件。对比IMERG产品,GSMaP产品在强降水事件中的表现整体上都要优于同级别的IMERG产品。本文为验证极端降水事件中遥感降水产品的精度提供了范例,并可以为遥感降水反演算法的进一步改进提供建议。

关键词: 湖南强降水误差标定误差分解GSMaP    
Abstract:

Heavy rainfall attacked Hunan province during late June to early July in 2017, causing various secondary disasters and severe financial losses. Here, three GSMaP (Global Satellite Mapping of Precipitation) datasets (i.e., GSMaP_NRT, GSMaP_MVK and GSMaP_Gauge) were investigated based on several statistical metrics and the error decomposition model, in an effort to analyze their error structure and variation characteristics, assess the capability of GSMaP products for monitoring heavy rain events. Results show that: (1) All datasets can well capture the spatial distribution and temporal characteristics of the heavy rainfall. (2) Due to the interference of orographic convection, all three datasets show uncertainties over mountainous regions. In the error structure, hit bias contributes most to the total error. (3) GSMaP_Gauge performs best for monitoring extremely heavy rainfall, missed error has a significant decrease after applying the gauge adjustments. Compared to IMERG products, GSMaP products shows much higher accuracy in monitoring heavy rainfall. We expected the results documented here can provide feedback for further improving the GSMaP retrieving algorithm and strengthening data quality during heavy rainfall periods.

Key words: Hunan    Heavy rainfall    Data calibration    Error decomposition    GSMaP
收稿日期: 2018-12-19 出版日期: 2019-12-05
ZTFLH:  TP79  
基金资助: 国家重点研发计划项目(2016YFC0401502);江苏水利科技项目(2017045)
通讯作者: 徐慧     E-mail: gaoyue@hhu.edu.cn;njxh@hhu.edu.cn
作者简介: 高 玥(1994-),女,吉林长春人,硕士研究生,主要从事遥感水文以及水生态领域方面的研究。E?mail:gaoyue@hhu.edu.cn
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引用本文:

高玥,徐慧,刘国. GSMaP遥感降水产品对典型极端降水事件监测能力评估[J]. 遥感技术与应用, 2019, 34(5): 1121-1132.

Yue Gao,Hui Xu,Guo Liu. Evaluation of the GSMaP Estimates on Monitoring Extreme Precipitation Events. Remote Sensing Technology and Application, 2019, 34(5): 1121-1132.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.1121        http://www.rsta.ac.cn/CN/Y2019/V34/I5/1121

图1  研究区地理概况
评价指标计算方法量纲最优值
相关系数(CC)CC=i=1nGi-GˉSi-Sˉi=1nGi-Gˉ2×i=1nSi-Sˉ21
均方根误差(RMSE)RMSE=1n×i=1nSi-Gi2mm0
平均误差(ME)ME=1ni=1nSi-Gimm0
相对偏差(BIAS)BIAS=i=1nSi-Gii=1nGi×100%%0
命中率(POD)POD=HH+M1
误报率(FAR)FAR=FH+F0
关键成功指数(CSI)CSI=HH+M+F1
表1  遥感降水误差标定体系各项评价指标定义
图2  累计降水量空间分布(2017年6月16日至7月15日)
图3  各流域平均累计降水量(2017年6月16日至7月15日)
图4  各流域逐日降水量变化折线图
图5  GSMaP产品各项评价指标(CC、ME、BIAS和RMSE)空间分布
图6  GSMaP各产品总误差及各误差成分空间分布
图7  GSMaP各产品与地面观测散点图
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
GSMaP_NRT0.49-3.9535.85-7.840.690.300.53
GSMaP_MVK0.58-0.2934.63-0.570.750.260.59
GSMaP_Gauge0.60-2.4931.08-4.830.850.240.67
IMERG-Early0.473.8538.427.800.760.310.56
IMERG-Late0.533.8836.447.620.790.270.61
IMERG-Final0.598.5434.2117.690.850.310.61
表2  大雨及以上级别强降水事件中GSMaP各套产品评价指标值
遥感降水产品

相对偏差

/%

偏差相对贡献率/%
命中漏测误报
GSMaP_NRT-7.84-8.57-12.5913.32
GSMaP_MVK-0.57-2.59-8.9310.94
GSMaP_Gauge-4.83-10.66-3.929.74
IMERG-Early7.801.63-9.6015.77
IMERG-Late7.622.69-7.7612.69
IMERG-Final17.696.24-4.8016.25
表3  大雨及以上级别强降水事件中GSMaP各套产品误差成分相对贡献率
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