地球科学进展, 2021, 36(6): 604-615 DOI: 10.11867/j.issn.1001-8166.2021.053

研究论文

TRMM遥感降水低估还是高估中国大陆地区的降水?

王忠静,1,2,3, 石羽佳1, 张腾1

1.清华大学水利系,北京 100084

2.清华大学水沙科学与水利水电工程国家重点实验室,北京 100084

3.青海大学省部共建三江源生态与高原农牧业国家重点实验室,青海 西宁 810016

Does TRMM Precipitation Underestimate or Overestimate in Mainland China?

WANG Zhongjing,1,2,3, SHI Yujia1, ZHANG Teng1

1.Department of Hydraulic Engineering,Tsinghua University,Beijing 100084,China

2.State Key Laboratory of Hydro-science and Engineering,Tsinghua University,Beijing 100084,China

3.State Key Laboratory of Plateau Ecology and Agriculture,Qinghai University,Xining 810016,China

收稿日期: 2021-01-14   修回日期: 2021-05-13   网络出版日期: 2021-07-22

基金资助: 宁夏重点研发计划项目“基于水联网全数字治水关键技术研究与示范”.  2020BCF01002
国家重点研发计划项目“西北典型地区节水与生态修复技术集成提升与规模示范”.  2016YFC0402900

Received: 2021-01-14   Revised: 2021-05-13   Online: 2021-07-22

作者简介 About authors

王忠静(1963-),男,山东莱芜人,教授,主要从事水文水资源研究.E-mail:zj.wang@tsinghua.edu.cn

WANGZhongjing(1963-),male,LaiwuCity,ShandongProvince,Professor.Researchareasincludehydrologyandwaterresources.E-mail:zj.wang@tsinghua.edu.cn

摘要

卫星降水产品在一定程度上为地表观测稀疏地区的降水提供了参照值,但在具体应用时仍需进行适用性和精度评价。为降低不确定性,通行做法是利用地面观测数据对卫星产品进行融合校正,评价和修正卫星降水产品。以TRMM为例,对研究中国大陆范围的融合TRMM降水的公开成果进行了检视,发现不同研究成果结论之间存在不可忽视的方向性相背情况,极大地影响着生产实践应用中对融合产品的可靠性判断。研究认为,这种融合结果相背的现象与融合校正方法关系不大,而与地面降水参照站点选取的范围有密切关系。研究表明,仅靠TRMM卫星降水自身及与地面融合方法的创新,尚不能降低卫星降水产品的不确定性。目前仍需加强对卫星降水融合中地面观测数据的完整性要求,采用多种独立检验方法验证融合结果的一致性和可靠性。

关键词: 遥感降水融合 ; TRMM ; 高估 ; 低估 ; 一致性检验

Abstract

Although satellite precipitation has provided a valuable reference for the precipitation estimation in the sparse region, its uncertainties in the practical application are still challenging. Researchers tried to fuse ground observation and satellite precipitation to evaluate and modify satellite precipitation products for uncertainty reduction. Unfortunately, this paper examined many publications of fusion TRMM precipitation products in mainland China and found a non-negligible directional contradiction between the approaches. The findings will significantly affect the reliability of satellite precipitation in practical applications. It was found that the contradiction has not related to the satellite precipitation products and fusion methods but significantly related to the selection of precipitation observation stations. It was also found that the uncertainty is hard to be reduced only by innovating the fusing methods of TRMM precipitation with ground observation data. Therefore, it is necessary to strengthen the integrity of ground observation and adopt independent practices to verify consistency and reliability.

Keywords: Satellite precipitation fusing ; TRMM ; Overestimate ; Underestimate ; Consistency validation

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本文引用格式

王忠静, 石羽佳, 张腾. TRMM遥感降水低估还是高估中国大陆地区的降水?. 地球科学进展[J], 2021, 36(6): 604-615 DOI:10.11867/j.issn.1001-8166.2021.053

WANG Zhongjing, SHI Yujia, ZHANG Teng. Does TRMM Precipitation Underestimate or Overestimate in Mainland China?. Advances in Earth Science[J], 2021, 36(6): 604-615 DOI:10.11867/j.issn.1001-8166.2021.053

1 引 言

降水是水循环的重要组成部分,在水资源管理中起着至关重要的作用。降水数据通常来源于地面雨量计观测,得到点尺度降水资料。为获得面降水情况,研究者通常利用插值技术对点降水资料再处理。但由于测站空间分布不均,在高海拔、低人口密度区域测站稀疏甚至缺失,这种插值将产生较大的或然性,特别在复杂地形下,通过插值描述区域降水特征会产生很大的误差。为克服这种不足,20世纪70年代起就有学者研究星载传感器的降水反演1~3,出现了GPCP(the Global Precipitation Climatology Project)4和CMAP(the CPC Merged Analysis of Precipitation)5等产品,提供了全球尺度的精度为2.5°×2.5°的月降水数据。1997年热带降雨观测卫星(Tropical Rainfall Measuring Mission, TRMM)搭载了世界上第一台星载降水雷达,开启了全球降水遥感监测时代6。随后,各种卫星观测产品陆续出现,如TMPA7、PERSIANN8、CMORPH9和GPM10等(表1),提供着广泛的全球降水数据。

表1   常用卫星降水产品一览

Table 1  List of common satellite precipitation products

产品名称空间分辨率时间分辨率数据起始时间
GPCP2.5°monthly1979年
CMAP2.5°monthly1979年
TMPA0.25°3 h1998年
PERSIANN0.25°3 h2002年
CMORPH0.25°/8 km30 min/3 h2002年
NRL-Blend0.1°3 h2003年
PERSIANN-CCS4 km30 min2003年
GSMAP0.1°1 h2005年
GPM0.1°30 min2015年

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卫星降水产品虽有其优势,但因其并非一手观测数据,是由反演得到的,因此存在不确定性11。为增强应用中的可靠性,常将卫星反演的面降水数据与地表观测的点降水数据融合,校正生产特定区域的降水融合产品。本文总结了卫星降水产品与地表观测降水数据融合校正方法及其在中国大陆地区的校正成果,经检视发现,众多卫星降水融合成果存在方向性相背现象,影响着实际应用中的可靠性,据此本文分析了这一现象产生的影响因素并提出了改进方法。

2 融合校正方法分类

2.1 偏差校正法

偏差校正法(Bias Correction)是指用观测数据计算估计值的加性或乘性等偏差因子对原始估计值重新标定以减少偏差,使修正后的估计值逼近测量值的方法。典型的偏差校正法包括加性偏差校正法(Additive Bias Correction)、乘性偏差校正法(Multiplicative Bias Correction)和分位数映射法(Quantile Mapping)。

加性偏差校正是用估计值与观测值之间的差值作为因子进行校正,乘性偏差校正则是通过一个比例因子使降水估计值具有与观测值相同的平均值。可以单独使用12~15,也可联合使用16,各有所长。分位数映射法利用历史数据建立观测数据和遥感产品数据的概率分布函数,通过概率映射(Probability Mapping)或直方图均衡化(Histogram Equalization)使两者分布匹配1718。该方法能够匹配估计值和观测值之间在月尺度的完整分布,同时设定了降水阈值,可避免因存在过多的极值而造成分布严重扭曲。

2.2 插值展布法

相对于偏差校正,插值(Interpolation)则是将点数据向线数据和面数据的展布,通常先用离散数据拟合连续函数,再用这个连续函数对缺测的离散点进行插补。在遥感数据和地面观测数据融合中,一般保留地面观测数据精度,引入遥感数据作为参考进行局部校正。

经典的插值方法是克里金法(Kriging)19,克里金法又常被称作为地统插值法(Geostatistical Interpolation)。克里金法后来发展出多种变形20~22和为减小测量稀疏区域融合过程中随机误差的辅助方法2324,均有诸多应用25~29。为使插值更加光滑、纠偏更加有效、计算更加快速,核平滑法(Kernel Smoothing)30、最优插值法31和最优概率插值法(Probability Density Function-Optimal Interpolation)3233等陆续被引入到卫星遥感产品插值融合中,提高了插值效果。

2.3 多元回归法

回归分析法(Regression Analysis)本属于插值法,但多元回归分析法(Multiple Regression Analysis)因其是结合物理机制中的多个影响因子来估计或预测目标值而被单列。

诸多研究发现,卫星降水产品的质量与纬度、海拔、坡向、坡度和下垫面组成等因素有关3435。多元回归分析法结合物理机制,挖掘大气(如温度、空气湿度、云层厚度等)、地理(如地形、经纬度、海拔、坡度、坡向等)、地表(如植被、土壤含水量、冰雪厚度等)等和地面观测数据之间的关系,以提高融合的精度。多元回归法可给出具体关系式,变量间的关系很容易被理解和解释,有诸多方法分支及应用36~49

2.4 机器学习法

与传统的插值或回归方法相比,机器学习法(Machine Learning)则倾向于利用其强大的学习能力和泛化能力,建立更为复杂的数据模式和处理海量数据。机器学习法可准确快速地处理高维特征空间,操作简单且容易建立多因素间的关系,但不易显现出变量间具体关系,可解释性弱50

常用的机器学习方法包括随机森林(Random Forest)、人工神经网络(Artificial Neural Network)、卷积神经网络(Convolutional Neural Network)和长短期记忆网络(Long-Short-Term Memory Network)等,诸多运用了不同的机器学习方法融合校正遥感产品数据和地面观测数据的研究均揭示了机器学习法在此方面效果良好454650~56

这4类方法特点及代表性应用如表2所列。

表2   降水数据融合校正方法特点及分类表

Table 2  Features and categories of precipitation data merging methods

方法分类代表性方法方法特点参考文献

偏差校正法

(Bias Correction)

加性偏差校正(Additive Bias Correction)观测值与估计值相差较低时适用12~16
乘性偏差校正(Multiplicative Bias Correction)研究尺度变化会对结果产生较大影响12~16
分位数映射(Quantile Mapping)不依赖预定函数及参考数据,运用灵活1718

插值展布法

(Interpolation)

普通克里金(Ordinary Kriging)克里金系列方法能考虑不同采样点之间空间属性的差异但受地面数据影响;协同克里金适用于变量较少的情况;外部漂移克里金法的外部空间变量必须光滑变化19~29
协同克里金(Co-Kriging)
外部漂移克里金(Kriging with External Drift)
贝叶斯克里金(Bayesian Kriging)
贝叶斯组合(Bayesian Combination)能量化不同来源数据估计值的不确定性23
反距离加权(Inverse Distance Weighting)可用于测量稀疏区域,能减小融合的随机误差,但受到地面站点分布密度影响24
反距离残差加权(Residual Inverse Distance Weighting)
核平滑(Kernel Smoothing)能改进有、无地面观测地区资料的一致性30
双核平滑(Double-Kernel Smoothing)
最优插值(Optimal Interpolation)线性无偏估计方面较好,融合时方差最小31~33
最优概率插值(Probability Density Function-optimal Interpolation)与概率密度函数结合能消除遥感地面结合时的时空分布偏倚误差

多元回归法

(Multiple Regression Analysis)

经验统计模型(Empirical Statistical Model)易解释,但不易体现空间分布差异性34354148
地理加权回归(Geographically Weighted Regression)直接解释空间变量间定量关系,计算灵活但计算过程较复杂36~4042~47
地理加权岭回归(Geographically Weighted Ridge Regression)

机器学习法

(Machine Learning)

随机森林(Random Forest)可以准确快速地处理高维特征空间,操作简单且容易建立较多影响因素之间的关系,但无法揭示变量之间的具体关系,不容易被理解和解释4455
人工神经网络(Artificial Neural Network)
卷积神经网络(Convolutional Neural Network)
长短期记忆网络(Long-Short-Term Memory Network)

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3 TRMM降水融合成果“背论”及影响因素

3.1 降水融合成果“背论”

在众多卫星降水产品中,TRMM降水产品应用最广泛,包括TRMM3B42和TRMM3B43全球格点化数据集57。其中,中国地区有194个测站数据加入了GPCC (Global Precipitation Climatology Centre)数据集以辅助TRMM卫星降水产品校正58

本文分析了部分发表在Web of Science核心集合数据库和中文核心期刊上的研究中国大陆的TRMM卫星降水代表性成果12~151827~29343539~4750~105,发现不同研究成果的结论颇有差异,出现了不可忽视的结论相背,称其为“背论”。此处“背论”不同于通常所说的“悖论”。前者是指对同一问题用相同或不同方法研究得到的结论相反的情形,而后者多指某种科学假设下,观测到的结果与预期结果相反的情形。

卫星遥感降水与地面观测降水相比,通常有3种情形:高估、低估和吻合。高估即认为所选用的卫星降水产品在研究区高估了实际降水;低估即认为所选用的卫星降水产品在研究区低估了实际降水;若卫星降水产品与真实降水值误差在±10%以内可视为与实际降水吻合,称之为吻合。

上述关于TRMM卫星降水产品对中国大陆降尺度应用或评价的研究结论如表3所列。

表3   TRMM 遥感降水产品对中国大陆区域降水估计的结论统计

Table 3  Statistics of TRMM products for precipitation estimation in mainland China

区域遥感降水产品高估低估吻合数量总计/个占比总计/%
数量/个占比/%数量/个占比/%数量/个占比/%
中国高海拔区TRMM 3B421350.0311.51038.526100.0
TRMM 3B42RT872.719.1218.211100.0
TRMM 3B431453.9830.8415.426100.0
总计3555.51219.11625.463100.0
中国低海拔区TRMM 3B42625.0312.51562.524100.0
TRMM 3B42RT457.1228.6114.37100.0
TRMM 3B43660.000.0440.010100.0
总计1639.0512.22048.841100.0

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由上可知,对于中国大陆高海拔地区,多数研究结论认为TRMM卫星降水的表达是高估的,占55.5%;认为吻合的占25.4%,而认为低估的,占19.1%。对于中国大陆低海拔地区,多数研究认为TRMM卫星降水的表达是吻合的,占48.8%;认为高估的占39%,而认为低估的仅占12.2%。可见,不同研究成果的结论存在较大的分歧。

本文从TRMM卫星遥感降水产品类型、研究区域、研究区高程范围、融合校正方法和观测数据来源等5个方面进一步分析“背论”产生的原因。

3.2 影响因素分析

(1)产品类型差别的影响。从表3可以看出,TRMM卫星的系列遥感降水产品,无论是实时产品3B42和3B42RT还是经地面站校正后的3B43产品,其结论的“背论”现象均有发生,只是高估、低估或吻合结论数量和比例有所变化。可见,这种“背论”的出现与TRMM卫星降水产品类型无关。对于其他卫星降水产品,本文暂不涉及。

(2)区域范围差别影响。不少成果认为TRMM高估了中国大陆地区的实际降水。如在高海拔地区的研究中,Tong等59、Ma等60和Wei等61通过分析认为3B42和3B42RT降水产品高估了整个青藏高原的降水量,石玉立等53认为3B43整体高估青藏高原降水;在中国低海拔地区的研究中,Yong等1270认为3B42数据在海拔较低的老哈河流域几乎全年都存在不切实际的高估情况,Tang等58认为3B42RT高估海拔较低的赣江流域的降水,Cao等77在长江三角洲地区研究中发现3B43高估了实际降水量。

然而,也有不少研究认为TRMM低估了中国大陆地区的实际降水。虽然持这种观点的成果比高估的少,但仍不可忽视。如Bharti等71认为3B42在喜马拉雅地区3 100 m高度以上低估降水,Ma等78认为3B43在青藏高原上整体低估降水,Xu等51认为3B42RT和3B43低估柴达木盆地高海拔地区降水。至今为止,少有文献认为3B43低估中国低海拔地区降水,小部分研究显示3B42系列产品低估了低海拔地区降水,如Jiang等13对湘江流域的研究和袁慧玲等72对淮河流域低海拔南部山区的研究。

此外,还有一些研究认为TRMM降水产品在对中国地区的降水估计是吻合的。在高海拔地区,Meng等86认为3B42对黄河源区的降水与实际吻合,Zhou等39认为3B42RT整体与柴达木盆地降水吻合,Jia等35认为3B43整体与柴达木盆地降水吻合,Wang等88在祁连山脉的研究显示3B43与研究区域的降水量吻合。在低海拔地区的研究中,诸多研究都认为遥感降水产品与真实降水产品几乎吻合,如Wu等50对中国东部低海拔地区的研究,Wang等89和Yang等91对珠江流域和汉江流域的研究,以及Chen等94对长江中下游的研究等。

本文将中国大陆区划分为7个分区,分别为西北地区、西南地区(不含西藏)、青藏地区、东北地区、华北地区、华中地区和华南地区,将各分区的高估、低估和吻合情况标注在图1中,分别用蓝色、黄色和橙色柱状图代表研究结果认为高估、低估和吻合降水的3种情况。

图1

图1   TRMM遥感降水产品研究在中国大陆各分区高低估及吻合情况分析

Fig. 1   Analysis of overestimate underestimate and coincidence of TRMM products in different regions of mainland China


图1可以看出,在TRMM降水产品研究中,在东北地区和华北地区成果较少,“背论”现象不甚明显但仍然存在;其他地区,西北、青藏、西南、华中和华南都存在明显的“背论”现象。从地理角度看,这种差异与研究范围无关。

(3)高程范围和融合方法差别影响。将研究对象按高程范围分为高海拔地区、中海拔地区和低海拔地区,分别统计其高估、低估和吻合情况,结果如图2a所示,同样再按照融合方法因子分类统计高估、低估和吻合情况,结果如图2b所示。

图2

图2   TRMM遥感降水产品融合校正及精度评价研究内容分析

(a) TRMM产品评价结果与高程范围关系;(b) TRMM产品评价结果与融合方法关系

Fig. 2   Analysis of merging and accuracy evaluation results of TRMM products

(a) Relationship between TRMM products evaluation results and elevation range; (b) Relationship between TRMM products evaluation results and merging methods


不难发现,“背论”的出现与高程范围没有明显的统计关系,无论是海拔较高、海拔中等还是海拔较低的地区均会出现“背论”现象;同样,无论是利用偏差校正、插值展布、多元回归和机器学习中的哪类方法的研究成果,也均存在“背论”现象;此外,单纯精度评价的研究成果也显示有高估、低估和吻合3种结论并存的现象。可见,这种“背论”现象与融合校正方法也无直接联系。

(4)观测数据来源差别影响。在遥感产品融合及精度评价研究中通常使用的地面观测数据有2类:一类是来自气象站点的降水观测数据,一类是来自水文站点的降水观测数据。限于数据易获得性,通常情况下大部分研究使用气象站点进行校正评价,少部分研究除气象站点数据外还补充了水文站点数据。本文针对地面观测数据来源这一影响因素进行了分析,统计结果如表4所列。聚焦青藏高原,将2种地表数据来源的研究成果的估计认同情况统计于表5

表4   降水观测数据源差别统计表

Table 4  Statistics of different precipitation observation data sources

数据情况遥感降水产品高估低估吻合数量总计/个占比总计/%
数量/个占比/%数量/个占比/%数量/个占比/%
只利用气象站观测数据TRMM 3B421744.737.91847.438100.0
TRMM 3B42RT1071.417.2321.414100.0
TRMM 3B431756.7620.0723.330100.0
总计4453.71012.22834.182100.0
包含水文站观测数据TRMM 3B42216.7325.0758.312100.0
TRMM 3B42RT250.0250.000.04100.0
TRMM 3B43350.0233.3116.76100.0
总计731.8731.8836.422100.0

新窗口打开| 下载CSV


表5   青藏地区研究成果降水观测数据来源差别统计表

Table 5  Statistics of different precipitation observation data sources in Qinghai-Tibet region

数据情况遥感降水产品高估低估吻合数量总计/个占比总计/%
数量/个占比/%数量/个占比/%数量/个占比/%
只利用气象站观测数据TRMM 3B42964.317.1428.614100.0
TRMM 3B42RT666.7111.1222.29100.0
TRMM 3B43660.0330.0110.010100.0
总计2163.6515.2721.233100.0
包含水文站观测数据TRMM 3B42133.300.0266.73100.0
TRMM 3B42RT00.01100.000.01100.0
TRMM 3B43250.0125.0125.04100.0
总计337.5225.0337.58100.0

新窗口打开| 下载CSV


表4可以发现,将总样本分为观测数据只来源于气象站点与包含水文站雨量观测数据这两类,其研究结论关于高估、低估和吻合的统计规律存在较大差异。只包含了气象站点数据的成果认为卫星产品高估、低估和吻合实际降水的所占总比例分别为53.7%、12.2%和34.1%,而加入了水文站点数据的结果的占比分别为31.8%、31.8%和36.4%。根据表5不难发现,在青藏地区研究中,只包含了气象站点数据的成果认为卫星产品高估、低估和吻合实际降水所占总比例分别为63.6%、15.2%和21.2%,而加入了水文站点数据的结果的各自占比为37.5%、25.0%和37.5%,两者分布趋势也有较大的不同。

这种情况在具体研究案例中也有同样体现,如孙美平等26用气象站点数据对TRMM3B43校正认为TRMM整体高估柴达木盆地降水,而Xu等51利用气象站降水数据和水文站降水数据对TRMM3B43降水数据与地面数据融合校正认为TRMM低估了柴达木盆地整体降水。根据以上分析,本文认为地面观测数据的来源是影响校正及评价结果的关键因素,是产生“背论”的关键因素。

4 讨 论

4.1 “背论”的启示

通过对产品类型、研究地区、高程范围、融合校正方法和观测数据来源这5个影响因素分析不难发现,无论是TRMM3B42、3B42RT还是3B43,均出现结论“背论”现象。这种“背论”的出现与TRMM卫星降水产品类型关系不显著,与地理区域关系不显著,与高程范围关系不显著,与校正方法关系也不显著。从目前分析看,地面观测数据的多寡和范围是影响校正及评价结论的关键因素。

以上可见,TRMM降水卫星融合成果的结论中“背论”是普遍存在的。这带给我们一个启示:在以方法推动的遥感降水融合研究中,方法创新性和自洽性尚不能消除结果的相背性,也不能消除由此带来的成果所表达的知识混乱性,更不能对实际应用中的数据带来可靠的辅助功能。这种“背论”现象既表明了卫星遥感降水传感器及其反演算法本身还需进一步改进,也表明了卫星遥感降水产品与地表观测数据的融合方法及可靠性仍属初步阶段,还表明了遥感降水产品的使用还应处于谨慎乐观状态。在自然科学研究层面,应寻找更为可靠的方法;在生产应用层面,应更加重视多渠道相互印证。

4.2 “背论”的消除

4.2.1 完善地表数据

地面观测数据取决于站点的选取,但雨量站点通常纵向分布不均匀,低海拔地区分布较为密集,高海拔地区站点稀疏,且无法覆盖研究区域全部高程范围,若只将有站点数据对应的遥感栅格精度评价结论延伸至海拔较高的区域则会产生无法预估的误差。以往的研究多专注于使用气象站的观测数据,较少有研究补充水文站点的数据。以柴达木盆地为例51,柴达木盆地内气象站与水文站高程分布范围不一,其中气象站平均高程为2 936 m,水文站的平均高程为3 162 m,水文站点总体高程要明显大于气象站点总体高程。在柴达木盆地的研究中加入水文站点的数据或自主测量的其他数据可以部分性地弥补观测数据在高程范围中的缺憾。同理,在其他地区的遥感降水融合校正及评价研究中,最好添加除气象站点数据以外的数据,尽可能地挖掘所有可利用的观测数据来源,最大限度地消除存在的误差。

4.2.2 增加检验方法

以往研究多注重融合校正方法的创新性和自洽性,在融合校正遥感产品、评价校正前后产品质量时只依靠同一组的站点数据,一旦加入其他来源的观测数据进行检验就可能出现与原结论不一致的情况。本文认为,在进行融合校正遥感产品研究时要进行多重检验,排除孤证。融合校正要使用2种及以上的独立检验方法,且检验结果所得趋势结论需相似,即需符合一致性检验。本文认为在遥感降水产品可行性评价中,有直接评价法也有间接评价法。直接评价法是指利用直接观测数据对遥感产品精度进行评价,如相对偏差(Relative Bias,RB)和均方根误差(Root Mean Square Deviation,RMSD)等;间接评价法是指利用再生数据进行检验,如利用水文模型模拟径流对遥感降水数据进行评价、基于其他降水产品进行检验106等方法。

典型的直接评价方法是利用站点数据对站点网格对应的遥感数据进行评价,如RB和RMSD等评价指标。这种评价简单、直接,是实证显示实证式评价,被公认为精度评价的最基本方法。然而,由于地表测站的分布、密度以及数据系列、数据质量等不同,这种评价也会产生一些难以破解的矛盾:有对应站点栅格的精度是否能代表无对应站点栅格的精度,稀疏站点对应的栅格精度是否能代表整个研究区域栅格站点的精度,以及当研究区域无站点时,如何评价。本文推荐增加一致性系数(Consistency Rate, CR)5155作为另一种独立检验方法补充验证评价,CR属于直接评价法,原理是通过站点处数据挖掘降水量与高程之间的关系,利用建立的降水—高程关系对无对应站点栅格的精度进行评价。该系数可以较好地检验站点以外网格的校正效果,弥补孤证判断的缺憾,其有效性已在以往工作中得到了验证5155

此外,不同的卫星产品多利用不同的数据源和反演算法,而不同的反演算法具有不同的性能,也各有优缺,在不同情形下(如下垫面情况、季节和经纬度等)、不同降水条件下(如冷季降水、弱降水、极强降水、地形降水和冷下垫面降水等)、不同的时空尺度上精度不同107。郭瑞芳等106认为比较不同产品在空间分布格局和时间变化趋势上的异同,能够获得待检产品的不确定性分布状况,尽管用于参照比较数据的精度并不明确,但得到的检验精度具有相对意义,有助于辨识待检产品的系统误差和随机误差。本文认为还可以增加基于其他降水产品进行检验的方法补充验证评价,该方法属于间接评价方法,原理是利用来源于不同传感器和反演算法的其他产品数据进行相互印证,可以减小产品本身的系统误差和随机误差。

综上,本文认为除常用的RB和RMSD等评价指标之外,可以添加一致性系数方法、基于其他降水产品进行检验的方法进行多重检验,提高研究结果的可靠性。

5 结 论

本文分析比较了遥感降水产品融合方法,通过对比分析TRMM卫星不同类型的遥感降水产品在中国不同研究区域、高程范围、融合校正方法和观测数据来源的研究成果发现,存在显著的结果“背论”。这种“背论”与产品类型、研究地区、高程范围和融合校正方法本身无明显关系,与观测数据来源有关。据此,本文认为若需减少或消除“背论”则需要注意以下两点:在研究中需要大力挖掘所有可利用的观测数据来源,补充除气象站点数据以外的数据,尽可能消除由于观测数据不完备所存在的误差。卫星降水融合校正结果检验应排除孤证,消除方法“自洽”的缺憾,需要利用多种独立证据、多重检验改进融合方法和验证产品可靠性。

除了TRMM系列降水产品外,其他卫星产品中也存在相似的“背论”,如Wang等48研究显示CMORPH大面积高估青藏高原降水,Tong等59研究则显示CMORPH显著低估青藏高原中部降水,可见卫星降水产品大多都存在很大误差。而以往研究多注重融合方法的研究而忽视了地面观测数据的完整性和多重检验的重要性,如此研究结果的准确性和科学性就会大打折扣。降水与水资源利用密切相关,例如洪水预报、农业和干旱监测、生态环境保护及恢复以及许多其他科学和社会应用等,准确的降水数据对于水资源管理、水旱灾害预报和自然灾害评价等相关研究至关重要,使用非直接测量的数据时既要保持谨慎态度,也要尽可能引入多方独立证据验证所用数据源的可靠性及代表性,解决数据不足和数据可靠性之间的矛盾。

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