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Article(id=1207627659488632923, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1207271180105499439, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2025.20242061, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1730563200000, receivedDateStr=2024-11-03, revisedDate=1734451200000, revisedDateStr=2024-12-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1765850470658, onlineDateStr=2025-12-16, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765850470658, onlineIssueDateStr=2025-12-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765850470658, creator=13701087609, updateTime=1765850470658, updator=13701087609, issue=Issue{id=1207271180105499439, tenantId=1146029695717560320, journalId=1205116964453384197, year='2025', volume='43', issue='9', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1765765479351, creator=13701087609, updateTime=1765765681303, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207272027254247478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1207271180105499439, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207272027254247479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1207271180105499439, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=11, endPage=14, ext={EN=ArticleExt(id=1207627659715125342, articleId=1207627659488632923, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Applicability Evaluation of GPM IMERG Satellite Precipitation Products in Jiangsu Province, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to evaluate the applicability of the latest generation GPM IMERG satellite precipitation products in Jiangsu Province, the measured precipitation data of 73 ground stations were selected as references. Using statistical indicators and extreme precipitation index, the accuracy evaluation of GPM IMERG precipitation products were conducted from the perspectives of day, month, season, and their extreme precipitation monitoring capabilities were evaluated. The results show that the GPM satellite precipitation products have good observation effects in Jiangsu region. At the daily and monthly scales, their overall and regional data have a high correlation with ground station measured data (CCC≥ 0.77), and the correlation becomes more significant with the increase of time scale. At the same time, the GPM satellites overestimate the measured precipitation, and the RRMSE at the monthly scale is relatively higher. There are certain differences in the observation accuracy of GPM satellites in different seasons, and the winter observation effect is the best, with high correlation and the lowest observation error (CCC=0.92, RRMSE=26.37 mm, RRB=23.4%). The GPM satellite has good ability to monitor extreme precipitation and can observe and describe the occurrence and intensity of extreme precipitation in Jiangsu region, especially in the southern Jiangsu region, with better observation effect.

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为评估最新一代GPM IMERG卫星降水产品在江苏省的适用性，选用73个地面站点实测降水数据为参考，利用统计学指标及极端降水指数，从日、月、季节等角度，对GPM IMERG降水产品整体及分区域进行精度评价，并评估其极端降水监测能力。结果表明，GPM卫星降水产品在江苏地区有较好的观测效果，在日、月尺度下，其整体及分区域与地面站点实测数据均具有较高的相关性（CCC≥0.77），且随着时间尺度的增加，相关性更为显著，同时GPM卫星对实测降水量存在高估现象，月尺度下的RRMSE相对更高。不同季节下GPM卫星观测精度存在一定差异，冬季观测效果最佳，具有高度相关性和最低的观测误差（CCC=0.92，RRMSE=26.37 mm，RRB=23.4%）。GPM卫星具有较好的极端降水监测能力，能够观测和描述江苏地区极端降水的发生及降水强度，特别是在苏南地区有更好的观测效果。

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丁彦文（1995-），女，硕士，研究方向为水文信息化，E-mail：

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丁彦文（1995-），女，硕士，研究方向为水文信息化，E-mail：

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丁彦文（1995-），女，硕士，研究方向为水文信息化，E-mail：

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Extreme precipitation index

, figureFileSmall=null, figureFileBig=null, tableContent=
指数定义单位
RR99P一年内日降水量序列的99%分位数mm
RR95P一年内日降水量序列的95%分位数mm
RX5day一年内最大连续5日降水量mm
R10一年内日降水量≥10 mm的天数d
R20一年内日降水量≥20 mm的天数d
R20TOT一年内日降水量≥20 mm的累计降水量mm
), ArticleFig(id=1207627666056913281, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1207627659488632923, language=CN, label=表1, caption=

极端降水指数

, figureFileSmall=null, figureFileBig=null, tableContent=
指数定义单位
RR99P一年内日降水量序列的99%分位数mm
RR95P一年内日降水量序列的95%分位数mm
RX5day一年内最大连续5日降水量mm
R10一年内日降水量≥10 mm的天数d
R20一年内日降水量≥20 mm的天数d
R20TOT一年内日降水量≥20 mm的累计降水量mm
), ArticleFig(id=1207627666140799367, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1207627659488632923, language=EN, label=Tab. 2, caption=

Accuracy evaluation of GPM satellite extreme precipitation monitoring capability

, figureFileSmall=null, figureFileBig=null, tableContent=
指数CCCRRMSE/mmRRB/%指数CCCRRMSE/mmRRB/%
RR99P0.7112.814.96R100.8613.4634.78
RR95P0.626.7830.38R200.698.6740.23
RX5day0.6549.567.26R20TOT0.72322.0536.49
), ArticleFig(id=1207627666258239890, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1207627659488632923, language=CN, label=表2, caption=

GPM卫星极端降水监测能力精度评价

, figureFileSmall=null, figureFileBig=null, tableContent=
指数CCCRRMSE/mmRRB/%指数CCCRRMSE/mmRRB/%
RR99P0.7112.814.96R100.8613.4634.78
RR95P0.626.7830.38R200.698.6740.23
RX5day0.6549.567.26R20TOT0.72322.0536.49
), ArticleFig(id=1207627666375680411, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1207627659488632923, language=EN, label=Tab. 3, caption=

Regional accuracy evaluation of GPM satellite extreme precipitation monitoring capability

, figureFileSmall=null, figureFileBig=null, tableContent=
极端降水指数分区CCCRRMSE/mmRRB/%极端降水指数分区CCCRRMSE/mmRRB/%
RR99P苏北0.7115.4219.04R10苏北0.6610.9634.98
 苏中0.5913.2220.78 苏中0.8014.2236.48
 苏南0.726.985.81 苏南0.8516.0533.45
RR95P苏北0.507.1536.58R20苏北0.417.5739.58
 苏中0.517.6035.29 苏中0.6210.4254.95
 苏南0.695.7121.06 苏南0.819.4736.13
RX5day苏北0.7048.3312.23R20TOT苏北0.68324.4938.39
 苏中0.7550.0618.62 苏中0.70362.7451.02
 苏南0.6550.66-2.96 苏南0.82296.4328.48
), ArticleFig(id=1207627666476343713, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1207627659488632923, language=CN, label=表3, caption=

GPM卫星极端降水监测能力分区域精度评价表

, figureFileSmall=null, figureFileBig=null, tableContent=
极端降水指数分区CCCRRMSE/mmRRB/%极端降水指数分区CCCRRMSE/mmRRB/%
RR99P苏北0.7115.4219.04R10苏北0.6610.9634.98
 苏中0.5913.2220.78 苏中0.8014.2236.48
 苏南0.726.985.81 苏南0.8516.0533.45
RR95P苏北0.507.1536.58R20苏北0.417.5739.58
 苏中0.517.6035.29 苏中0.6210.4254.95
 苏南0.695.7121.06 苏南0.819.4736.13
RX5day苏北0.7048.3312.23R20TOT苏北0.68324.4938.39
 苏中0.7550.0618.62 苏中0.70362.7451.02
 苏南0.6550.66-2.96 苏南0.82296.4328.48
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GPM IMERG卫星降水产品在江苏省的适用性评价
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丁彦文 , 胡宜娜 , 李婧 , 沈永捷 , 王培 , 朱月
水电能源科学 | 水文水资源与环境 2025,43(9): 11-14
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水电能源科学 | 水文水资源与环境 2025, 43(9): 11-14
GPM IMERG卫星降水产品在江苏省的适用性评价
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丁彦文 , 胡宜娜, 李婧, 沈永捷, 王培, 朱月
作者信息
  • 江苏省水文水资源勘测局，江苏 南京 210000

    • 丁彦文（1995-），女，硕士，研究方向为水文信息化，E-mail：

Applicability Evaluation of GPM IMERG Satellite Precipitation Products in Jiangsu Province
Yan-wen DING , Yi-na HU, Jing LI, Yong-jie SHEN, Pei WANG, Yue ZHU
Affiliations
  • Jiangsu Province Hydrology and Water Resources Investigation Bureau, Nanjing 210000, China
出版时间: 2025-09-25 doi: 10.20040/j.cnki.1000-7709.2025.20242061
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摘要
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为评估最新一代GPM IMERG卫星降水产品在江苏省的适用性，选用73个地面站点实测降水数据为参考，利用统计学指标及极端降水指数，从日、月、季节等角度，对GPM IMERG降水产品整体及分区域进行精度评价，并评估其极端降水监测能力。结果表明，GPM卫星降水产品在江苏地区有较好的观测效果，在日、月尺度下，其整体及分区域与地面站点实测数据均具有较高的相关性（CCC≥0.77），且随着时间尺度的增加，相关性更为显著，同时GPM卫星对实测降水量存在高估现象，月尺度下的RRMSE相对更高。不同季节下GPM卫星观测精度存在一定差异，冬季观测效果最佳，具有高度相关性和最低的观测误差（CCC=0.92，RRMSE=26.37 mm，RRB=23.4%）。GPM卫星具有较好的极端降水监测能力，能够观测和描述江苏地区极端降水的发生及降水强度，特别是在苏南地区有更好的观测效果。

GPM  /  IMERG  /  降水强度  /  极端降水  /  江苏

In order to evaluate the applicability of the latest generation GPM IMERG satellite precipitation products in Jiangsu Province, the measured precipitation data of 73 ground stations were selected as references. Using statistical indicators and extreme precipitation index, the accuracy evaluation of GPM IMERG precipitation products were conducted from the perspectives of day, month, season, and their extreme precipitation monitoring capabilities were evaluated. The results show that the GPM satellite precipitation products have good observation effects in Jiangsu region. At the daily and monthly scales, their overall and regional data have a high correlation with ground station measured data (CCC≥ 0.77), and the correlation becomes more significant with the increase of time scale. At the same time, the GPM satellites overestimate the measured precipitation, and the RRMSE at the monthly scale is relatively higher. There are certain differences in the observation accuracy of GPM satellites in different seasons, and the winter observation effect is the best, with high correlation and the lowest observation error (CCC=0.92, RRMSE=26.37 mm, RRB=23.4%). The GPM satellite has good ability to monitor extreme precipitation and can observe and describe the occurrence and intensity of extreme precipitation in Jiangsu region, especially in the southern Jiangsu region, with better observation effect.

GPM  /  IMERG  /  precipitation intensity  /  extreme precipitation  /  Jiangsu
丁彦文, 胡宜娜, 李婧, 沈永捷, 王培, 朱月. GPM IMERG卫星降水产品在江苏省的适用性评价. 水电能源科学, 2025 , 43 (9) : 11 -14 . DOI: 10.20040/j.cnki.1000-7709.2025.20242061
Yan-wen DING, Yi-na HU, Jing LI, Yong-jie SHEN, Pei WANG, Yue ZHU. Applicability Evaluation of GPM IMERG Satellite Precipitation Products in Jiangsu Province[J]. Water Resources and Power, 2025 , 43 (9) : 11 -14 . DOI: 10.20040/j.cnki.1000-7709.2025.20242061
正文
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1 概况
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江苏省地势平坦，河湖众多，地貌以平原、水域、低山丘陵为主。江苏省属于东亚季风气候区，处于亚热带与暖温带气候过渡地带，气候温和，四季分明，全年降水量季节变化特征显著，夏季降水量集中，约占全年降水量的一半，冬季降水量最少，约占全年降水量的1/10。目前关于江苏最新一代GPM IMERG降水数据集的精度评价研究比较缺乏，已有研究多为GPM卫星空间降尺度[1]及卫星反演算法适用性评价[2]，且少有细分区域对GPM卫星进行适用性研究，具有很大的探索空间。鉴此，本文以江苏省为研究区，基于地面雨量站实测数据，从不同角度对IMERG降水数据产品进行整体及分区域的精度评价，研究可为旱涝灾害防御、水资源优化配置等提供借鉴。
2 数据与方法
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2.1 研究数据
使用江苏省73个地面雨量站点2020年1月1日至2022年12月31日的实测日降水量数据（图1），对GPM卫星降水数据进行评价。
在GPM计划中，IMERG是其中新一代集合多卫星的反演降水产品，其融合了多种微波、红外和雷达数据，充分发挥了多源传感器的优势互补。选用2020年1月1日至2022年12月31日的日尺度IMEGR Final Run最新V07B版本产品（下载地址：https://disc.gsfc.nasa.gov），空间分辨率为0.1°×0.1°，该产品经过全球降水气候中心的月均站点进行了误差校正，有较高的数据精度。由于地面站点数据与GPM卫星降水数据存在空间差异，因此利用ArcGIS中Extract Multi Values to Points工具，以地面雨量站位置为基准，选用双线性插值法对GPM栅格数据进行插值，获得73个站点对应的GPM日降水数据，并在时间尺度上进行累加，获得逐日、逐月、逐季的地面站点及对应的GPM卫星降水数据[3]。
2.2 研究方法
选用当前卫星降水研究领域常用的统计学指标和极端降水指标对GPM卫星降水产品进行适用性评价。选用相关系数CCC评价地面站点降水数据与GPM卫星降水数据之间的相关性；选用均方根误差RRMSE来衡量地面站点降水数据与GPM降水数据之间的离散程度，可以反映卫星降水产品整体误差和精度；选用相对偏差RRB来揭示地面站点降水数据与GPM降水数据间误差的变化趋势，量化卫星降水产品高低估现象。
在全球变暖趋势下，极端天气愈加频发，成为全球共同应对的难题，我国大部分地区极端降水事件也呈显著增加趋势。因此探究GPM卫星对极端降水的监测能力是评价其在研究区内适用性的重要方面。根据研究区降水特征，选用6个极端降水指数来评价GPM降水卫星对江苏省极端降水情况的监测能力，各指标见表1。
3 结果与分析
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3.1 日、月尺度下GPM卫星降水产品精度评价
为评估GPM卫星降水产品在江苏省的精度，将73个地面雨量站点实测数据与卫星降水数据在日、月尺度上进行对比分析，结果见图2。可以看出，由日尺度到月尺度，GPM降水数据对实测降水数据的拟合度明显改善，数据离散程度减小。相关性方面，日尺度下CCC为0.79，月尺度CCC为0.94，表明无论在日尺度还是月尺度下，GPM卫星降水数据与地面站点实测数据均具有相关性，且随着时间尺度的增加，相关性更为显著，在月尺度下两者呈高度相关。进一步分析GPM降水数据与地面实测数据的差异，对比均方根误差，日尺度下RRMSE较小，为7.4 mm，月尺度下RRMSE=59.28 mm，相对较大；从相对偏差角度来看，GPM降水产品对地面实测降水量存在高估现象，在日、月尺度下RRB均为38.63%。分析误差存在的原因，可能是由于地面站点实测降水数据与GPM栅格网降水数据间存在时空差异，且目前GPM产品校正算法是基于月尺度站点数据校正的结果。
3.2 不同季节GPM卫星降水产品精度对比
江苏省年降水量充沛，且降水季节分布特征明显，降水多集中分布在5~10月，11月至次年4月降水较少。因此，分析不同季节GPM卫星降水产品的精度是判断其在江苏地区适用性的重要方面。
根据地区气候特征将3~5月划分为春季，6~8月划分为夏季，9~11月划分为秋季，12月至次年2月划分为冬季，并对不同季节下地面站点降水数据与GPM降水数据进行线性回归分析，结果见图3。可以看出，GPM卫星降水产品在不同季节的观测精度存在一定差异，其中冬季拟合度最佳，相关性最高，CCC高达0.92，RRMSE、RRB均为四个季节中最低，分别为26.37 mm、23.4%。由于江苏省全年降水量在冬季最低，而GPM卫星搭载双频降水测量雷达，采用高敏感度模式交错采样，有效提高了对弱降水和固态降水的探测能力，因此在冬季少雨期有较高的测量精度，这与刘益锋等[4]的研究结果一致。
在春季和夏季，GPM降水产品与地面实测数据相关性较高，CCC分别为0.89、0.88，秋季相关性相对其他季节较低，CCC=0.86。从误差角度来看，夏季RRMSE最高，达到257.02 mm，而RRB相对较低，仅次于冬季，RRB=33.55%，这可能是由于江苏省夏季降水多，暴雨频繁，且常伴随台风，导致卫星在观测精度上存在不同程度的高估和低估现象。春季GPM降水数据与地面实测数据虽然有较高的相关性，但高估现象较为严重，RRB=56.68%，RRMSE=121.39 mm。秋季误差相对较低，RRMSE=81.89 mm，RRB=34.78%。
3.3 极端降水监测能力分析评价
GPM卫星与地面观测站点极端降水指数的散点图见图4，精度分析评价结果见表2。可以看出，GPM卫星降水产品在各极端降水指标下与地面站点间均具有一定的相关性，相关系数CCC均超过0.6，其中RR95P相关性最低，CCC=0.62，R10的CCC最高，为0.86。从误差的角度来看，RR95P和R20的RRMSE较低，分别为6.78、8.67 mm，RR99P和R10指标下GPM卫星也表现出较好的监测效果，RRMSE分别为12.8、13.46 mm，而R20TOT和RX5day的RRMSE较高，分别为322.05、49.56 mm，存在较大的误差不确定性，分析原因可能是由于卫星异常的高估与低估会对累计多日的极端降水指标产生较大影响。对比分析RRB，GPM降水卫星在各指标下均存在高估现象，其中高估最严重的指标为R20，RRB= 40.23%，偏差程度最小的指标为RX5day，RRB= 7.26%。总体而言，GPM降水卫星对于江苏地区极端降水具有一定的描述能力，能够较好地监测极端降水的发生及降水强度。
3.4 分区域GPM卫星降水产品适用性分析
江苏省地跨北纬30°45'~35°08'，在地理上跨越南北，这一特殊的地理位置使得其在气候、植被等方面存在较大的南北差异，降水分布存在南多北少的特征。为了研究的准确性，将研究区分为苏北、苏中、苏南三个区域进一步对GPM降水产品进行分析。
结果见图5。在日尺度下，苏中和苏北地区GPM降水数据与实测降水数据的相关性更高，CCC分别为0.81、0.80，而苏南CCC相对较低，为0.77。误差方面，苏北RRMSE相对最高为7.58 mm，RRB=40.25%；苏中RRMSE最低，为6.63 mm，但其RRB相对最高，为47.18%；苏南RRB最低，为35.01%，RRMSE=7.34 mm。在月尺度下，GPM与实测降水数据在各区域的拟合程度有明显改善，均具有高度相关性（CCC≥0.94），苏中相关性最高，CCC=0.95。误差方面，苏南误差相对最低，RRMSE=57.95 mm，RRB=35.01%；苏中误差最高，RRMSE=62.19 mm，RRB=47.18%。
整体来看，GPM降水产品在江苏省与分区域中的拟合结果具有相似特征，随着时间尺度的增加，数据离散程度均有减小，相关性明显提高。此外，在日、月尺度下，苏中地区相关性均有所提高，苏南地区误差精度均有所降低。
由于苏南相对苏北、苏中地区极端降水发生的频次和强度更高，为了进行更准确的评价，分区域对GPM降水产品极端降水监测能力进行探究。根据极端降水指数，分区域对GPM降水数据与地面站点数据进行计算，并进行线性拟合，精度评价结果见表3。可以看出，在苏南地区GPM卫星线性拟合结果精度相对更好，CCC≥0.65，在除RX5day以外的五个指数下，相关性均为最高，在RR99P、RR95P、R20TOT指数下均具有最低的RRMSE、RRB。说明GPM降水产品在江苏省极端降水更多的地区有更好的观测效果，具有较好的极端降水监测能力。
4 结论
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a. 在对江苏整体及分区域的研究中，GPM降水数据与地面站点实测数据均具有较高的相关性，且随着时间尺度的增加，相关性更为显著。
b. 不同季节下GPM卫星观测精度存在一定差异，其中冬季观测效果最佳。
c. GPM卫星能够较好地观测和描述江苏地区极端降水的发生及降水的强度，且在苏南地区有更高的观测精度。
d. 整体而言，GPM IMERG卫星降水产品在江苏省具有较好的观测效果，能够为该地区降水监测、降水过程再现等工作提供数据支持，具有一定的应用潜力。
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参考文献 引证文献
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孙桂凯, 魏义熊, 王国帅, 等. GPM IMERG卫星降水数据在广西地区适用性分析[J]. 人民长江, 2022, 53(7): 98-106.
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doi: 10.20040/j.cnki.1000-7709.2025.20242061
  • 接收时间：2024-11-03
  • 首发时间：2025-12-16
  • 出版时间：2025-09-25
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  • 收稿日期：2024-11-03
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    江苏省水文水资源勘测局，江苏 南京 210000
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genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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