TRMM遥感降水低估还是高估中国大陆地区的降水?
1.
2.
3.
Does TRMM Precipitation Underestimate or Overestimate in Mainland China?
1.
2.
3.
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: |
|
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
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:
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:
本文引用格式
王忠静, 石羽佳, 张腾.
WANG Zhongjing, SHI Yujia, ZHANG Teng.
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TMPA[7]、PERSIANN[8]、CMORPH[9]和GPM[10]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
产品名称 | 空间分辨率 | 时间分辨率 | 数据起始时间 |
---|---|---|---|
GPCP | 2.5° | monthly | 1979年 |
CMAP | 2.5° | monthly | 1979年 |
TMPA | 0.25° | 3 h | 1998年 |
PERSIANN | 0.25° | 3 h | 2002年 |
CMORPH | 0.25°/8 km | 30 min/3 h | 2002年 |
NRL-Blend | 0.1° | 3 h | 2003年 |
PERSIANN-CCS | 4 km | 30 min | 2003年 |
GSMAP | 0.1° | 1 h | 2005年 |
GPM | 0.1° | 30 min | 2015年 |
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)。
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。
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。
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]。
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
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 | [17,18] | |
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 | [34,35,41,48] |
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 | [36~40,42~47] | |
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 | [44,55] |
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) |
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”
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
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 3B42 | 13 | 50.0 | 3 | 11.5 | 10 | 38.5 | 26 | 100.0 |
TRMM 3B42RT | 8 | 72.7 | 1 | 9.1 | 2 | 18.2 | 11 | 100.0 | |
TRMM 3B43 | 14 | 53.9 | 8 | 30.8 | 4 | 15.4 | 26 | 100.0 | |
总计 | 35 | 55.5 | 12 | 19.1 | 16 | 25.4 | 63 | 100.0 | |
中国低海拔区 | TRMM 3B42 | 6 | 25.0 | 3 | 12.5 | 15 | 62.5 | 24 | 100.0 |
TRMM 3B42RT | 4 | 57.1 | 2 | 28.6 | 1 | 14.3 | 7 | 100.0 | |
TRMM 3B43 | 6 | 60.0 | 0 | 0.0 | 4 | 40.0 | 10 | 100.0 | |
总计 | 16 | 39.0 | 5 | 12.2 | 20 | 48.8 | 41 | 100.0 |
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。
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
从图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。
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。
表4 降水观测数据源差别统计表
Table 4
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 3B42 | 17 | 44.7 | 3 | 7.9 | 18 | 47.4 | 38 | 100.0 |
TRMM 3B42RT | 10 | 71.4 | 1 | 7.2 | 3 | 21.4 | 14 | 100.0 | |
TRMM 3B43 | 17 | 56.7 | 6 | 20.0 | 7 | 23.3 | 30 | 100.0 | |
总计 | 44 | 53.7 | 10 | 12.2 | 28 | 34.1 | 82 | 100.0 | |
包含水文站观测数据 | TRMM 3B42 | 2 | 16.7 | 3 | 25.0 | 7 | 58.3 | 12 | 100.0 |
TRMM 3B42RT | 2 | 50.0 | 2 | 50.0 | 0 | 0.0 | 4 | 100.0 | |
TRMM 3B43 | 3 | 50.0 | 2 | 33.3 | 1 | 16.7 | 6 | 100.0 | |
总计 | 7 | 31.8 | 7 | 31.8 | 8 | 36.4 | 22 | 100.0 |
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
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 3B42 | 9 | 64.3 | 1 | 7.1 | 4 | 28.6 | 14 | 100.0 |
TRMM 3B42RT | 6 | 66.7 | 1 | 11.1 | 2 | 22.2 | 9 | 100.0 | |
TRMM 3B43 | 6 | 60.0 | 3 | 30.0 | 1 | 10.0 | 10 | 100.0 | |
总计 | 21 | 63.6 | 5 | 15.2 | 7 | 21.2 | 33 | 100.0 | |
包含水文站观测数据 | TRMM 3B42 | 1 | 33.3 | 0 | 0.0 | 2 | 66.7 | 3 | 100.0 |
TRMM 3B42RT | 0 | 0.0 | 1 | 100.0 | 0 | 0.0 | 1 | 100.0 | |
TRMM 3B43 | 2 | 50.0 | 1 | 25.0 | 1 | 25.0 | 4 | 100.0 | |
总计 | 3 | 37.5 | 2 | 25.0 | 3 | 37.5 | 8 | 100.0 |
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)[51,55]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[51,55]。
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:
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。
参考文献
A satellite technique for quantitatively mapping rainfall rates over oceans
[J]. ,
Special sensor microwave imager derived global rainfall estimates for climatological applications
[J]. ,
Satellite retrieval of precipitation: an overview
[J]. ,
卫星遥感反演降水研究综述
[J]. ,
The Global Precipitation Climatology Project (GPCP) combined precipitation dataset
[J]. ,
Global precipitation: a 17-year monthly analyses based on gauge observations, satellite estimates, and numerical model outputs
[J]. ,
The Tropical Rainfall Measuring Mission (TRMM) sensor package
[J]. ,
The TRMM Multisatellite Precipitation Analysis (TMPA): quasi global, multiyear, combined-sensor precipitation estimates at fine scales
[J]. ,
Evaluation of PERSIANN system satellite—Based estimates of tropical rainfall
[J]. ,
CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution
[J]. ,
Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation
[Z].
Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response
[J]. ,
Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin
[J]. ,
Improvement of multi-satellite real-time precipitation products for ensemble streamflow simulation in a middle latitude basin in South China
[J]. ,
Evaluation of reanalysis and satellite-based precipitation datasets in driving hydrological models in a humid region of Southern China
[J]. ,
Proportional coefficient method applied to TRMM rainfall data: case study of hydrological simulations of the Hotan River Basin (China)
[J]. ,
Resampling of regional climate model output for the simulation of extreme river flows
[J]. ,
Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?
[J]. ,
Combining satellite precipitation and long-term ground observations for hydrological monitoring in China
[J]. ,
Optimal areal rainfall estimation using raingauges and satellite data
[J]. ,
A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements
[J]. ,
Hydrologic evaluation of TRMM multisatellite precipitation analysis for Nanliu River Basin in Humid Southwestern China
[J]. ,
Spatial and seasonal distribution characteristics of precipitation over the Qinghai-Tibet Plateau based on TRMM data
[J]. ,
基于TRMM数据的青藏高原降水的空间和季节分布特征
[J].,
Study on maximum precipitation height zone in Qilian Mountains area based on TRRM precipitation data
[J]. ,
基于TRMM降水订正数据的祁连山地区最大降水高度带研究
[J].,
Spatiotemporal interpolation of rainfall by combining BME theory and satellite rainfall estimates
[J]. ,
Evaluation of TRMM precipitation and its application to distributed hydrological model in Naqu River Basin of the Tibetan Plateau
[J]. ,
Tracking the error sources of spatiotemporal differences in TRMM accuracy using error decomposition method
[J]. ,
Spatio-temporal modelling of spatially aggregate birth data
[J]. ,
A merging scheme for constructing daily precipitation analyses based on objective bias-correction and error estimation techniques
[J]. ,
A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses
[J]. ,
A high spatiotemporal gauge-satellite merged precipitation analysis over China
[J]. ,
First evaluation of the climatological calibration algorithm in the real-time TMPA precipitation estimates over two basins at high and low latitudes
[J]. ,
A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China
[J]. ,
Geographically weighted regression: a method for exploring spatial nonstationarity
[J]. ,
A comparison of random coefficient modelling and geographically weighted regression for spatially non-stationary regression problems
[J]. ,
Diagnostic tools and a remedial method for collinearity in geographically weighted regression
[J]. ,
Daily rainfall model to merge TRMM and ground based observations for rainfall estimations
[C]//
Geographically weighted regression based methods for merging satellite and gauge precipitation
[J]. ,
Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau
[J]. ,
Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity
[J]. ,
A rainfall model based on a geographically weighted regression algorithm for rainfall estimations over the Arid Qaidam Basin in China
[J]. ,
Spatial downscaling of TRMM precipitation data using an optimal subset regression model with NDVI and terrain factors in the Yarlung Zangbo River Basin, China
[J]. ,
Fine-resolution precipitation mapping in a mountainous watershed: geostatistical downscaling of TRMM products based on environmental variables
[J]. ,
An improved statistical downscaling scheme of tropical rainfall measuring mission precipitation in the Heihe River Basin, China
[J]. ,
A downscaling-merging method for high-resolution daily precipitation estimation
[J]. ,
Improving daily precipitation estimates for the Qinghai‐Tibetan Plateau based on environmental similarity
[J]. ,
RF-MEP: a novel Random Forest method for merging gridded precipitation products and ground-based measurements
[J]. ,
A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China
[J]. ,
Mapping areal precipitation with fusion data by ANN machine learning in sparse gauged region
[J]. ,
Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River
[J]. ,
Calibration of TRMM
1998—2012年青藏高原TRMM3B43降水数据的校准
[J].,
Using the back propagation neural network approach to bias correct TMPA data in the arid region of Northwest China
[J]. ,
Topography and data mining based methods for improving satellite precipitation in mountainous areas of China
[J]. ,
Evaluation and hydrological application of precipitation estimates derived from PERSIANN‐CDR, TRMM 3B42V7, and NCEP‐CFSR over humid regions in China
[J]. ,
Quality assessment of hourly merged precipitation product over China
[J]. ,
Statistical and Hydrological comparisons between TRMM and GPM level-3 products over a Midlatitude Basin: is Day-1 IMERG a good successor for TMPA 3B42V7?
[J]. ,
Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau
[J]. ,
Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau
[J]. ,
Evaluation of satellite—Based precipitation products from IMERG V04A and V03D, CMORPH and TMPA with gauged rainfall in three climatologic zones in China
[J]. ,
Bayesian assimilation of multiscale precipitation data and sparse ground gauge observations in mountainous areas
[J]. ,
Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network
[J]. ,
Inter-comparison and evaluation of remote sensing precipitation products over China from 2005 to 2013
[J]. ,
Accuracy evaluation of precipitation analysis in Yangtze River Basin based on satellite TRMM
[J]. ,
基于TRMM卫星产品的长江流域降水精度评估
[J].,
Applicability analysis of multi-satellite remote sensing precipitation data in Tarim River Basin
[J]. ,
多卫星遥感降水数据在塔里木河流域的适用性分析
[J].,
How do GPM IMERG precipitation estimates perform as hydrological model forcing? evaluation for 300 catchments across Mainland China
[J]. ,
Research on accuracy validation and calibration methods of TRMM in Tianshan Mountains of Xinjiang
[J]. ,
新疆天山山区TRMM卫星降水数据的精度检验和校正方法研究
[J].,
Accuracy evaluation and seasonal distribution of precipitation over the Mongolian Plateau based on TRMM data
[J]. ,
基于TRMM卫星数据的蒙古高原降水精度评估与季节分布特征
[J].,
Hydrologic evaluation of multisatellite precipitation analysis standard precipitation products in basins beyond its inclined latitude band: a case study in Laohahe Basin, China
[J]. ,
Evaluation of error in TRMM 3B42V7 precipitation estimates over the Himalayan region
[J]. ,
Application of satellite station fusion data evaluation and hydrological simulation in the upper reaches of Huaihe River basin
[C]//Chinese Meteorological Society.
淮河上游流域卫星站点融合资料评估及水文模拟应用
[C]//中国气象学会.
Comprehensive precipitation evaluation of TRMM 3B42 with dense rain gauge networks in a mid-latitude basin, northeast, China
[J]. ,
Assessment of satellite-derived precipitation products for the Beijing region
[J]. ,
Assessing the applicability of different sources of precipitation data in Aksu River Basin on south slope of Tianshan Mountains
[J]. ,
不同源降水数据在天山南坡阿克苏河流域适用性分析
[J].,
Evaluation and correction of the TRMM 3B43V7 and GPM 3IMERGM satellite precipitation products by use of ground—Based data over Xinjiang, China
[J]. ,
Evaluation of TRMM 3B43 data over the Yangtze River Delta of China
[J]. ,
A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai-Tibet Plateau with the effects of systematic anomalies removed
[J]. ,
Accuracy analysis of TRMM
TRMM卫星3B43降水数据在黄河流域的精度分析
[J].,
Accuracy of TRMM precipitation data in the southwest monsoon region of China
[J]. ,
Characterizing spatial patterns of precipitation based on corrected TRMM 3B43 data over the Mid Tianshan Mountains of China
[J]. ,
Evaluation and intercomparison of high-resolution satellite precipitation estimates—GPM, TRMM, and CMORPH in the Tianshan Mountain area
[J]. ,
Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) products and their potential hydrological application at an arid and semiarid basin in China
[J]. ,
Analysis of the accuracy of TRMM 3B42 rainfall data in the upper and middle reaches of Taohe River
[J]. ,
TRMM 3B42降水产品在洮河中上游的精度评估分析
[J].,
Analysis of the accuracy of TRMM 3B43 precipitation data in the source region of the Yellow River
[J]. ,
TRMM 3B43降水数据在黄河源区的适用性评价
[J].,
Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River
[J]. ,
Evaluation of GPM IMERG V05B and TRMM 3B42V7 precipitation products over high mountainous tributaries in Lhasa with dense rain gauges
[J]. ,
An improved spatial-temporal downscaling method for TRMM precipitation datasets in alpine regions: a case study in Northwestern China's Qilian Mountains
[J]. ,
Evaluation and hydrologic validation of TMPA satellite precipitation product downstream of the Pearl River Basin, China
[J]. ,
Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang Lake Basin
[J]. ,
Evaluation of the TRMM multisatellite precipitation analysis and its applicability in supporting reservoir operation and water resources management in Hanjiang Basin, China
[J]. ,
Evaluation of the latest satellite-gauge precipitation products and their hydrologic applications over the Huaihe River Basin
[J]. ,
Multiscale comparative evaluation of the GPM IMERG v5 and TRMM 3B42 v7 precipitation products from 2015 to 2017 over a climate transition area of China
[J]. ,
A new downscaling-integration framework for high-resolution monthly precipitation estimates: combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data
[J]. ,
Evaluation of precipitation products by using multiple hydrological models over the upper Yellow River Basin, China
[J]. ,
Spatiotemporal patterns of satellite precipitation extremes in the Xijiang River Basin: from statistical characterization to stochastic behaviour modelling
[J]. ,
Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China
[J]. ,
Capacity of satellite—Based and reanalysis precipitation products in detecting long-term trends across Mainland China
[J]. ,
Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China
[J]. ,
Nine-year systematic evaluation of the GPM and TRMM precipitation products in the Shuaishui River Basin in East-Central China
[J]. ,
Reliability of gridded precipitation products in the Yellow River Basin, China
[J]. ,
Assessment of high-resolution satellite rainfall products over a gradually elevating mountainous terrain based on a high-density rain gauge network
[J]. ,
MULTIdimensional evaluation of the TRMM 3B43V7 satellite-based precipitation product in mainland China from 1998-2016
[J]. ,
Applicability analysis of multiple precipitation products in the Qaidam Basin, Northwestern China
[J]. ,
Evaluation of multi-source precipitation products over the Yangtze River Basin
[J]. ,
Strategy and method for satellite precipitation data evaluation: an overview
[J]. ,
遥感降水数据精度检验策略及检验方法综述
[J].,
/
〈 | 〉 |