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

研究论文 上一篇    下一篇

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. 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
  • 收稿日期:2021-01-14 修回日期:2021-05-13 出版日期:2021-06-10
  • 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)

Does TRMM Precipitation Underestimate or Overestimate in Mainland China?

Zhongjing WANG 1, 2, 3( ),Yujia SHI 1,Teng ZHANG 1   

  1. 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
  • Received:2021-01-14 Revised:2021-05-13 Online:2021-06-10 Published:2021-07-22
  • About author:WANG Zhongjing (1963-), male, Laiwu City, Shandong Province, Professor. Research areas include hydrology and water resources. E-mail: zj.wang@tsinghua.edu.cn
  • Supported by:
    the Key Research and Development Program of Ningxia "Research and demonstration of key technology of digital water governance based on internet of water"(2020BCF01002);The National Key Research and Development Program "Integrated promotion and scale demonstration of water saving and ecological restoration technology in typical areas of Northwest China"(2016YFC0402900)

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。

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.

中图分类号: 

表1 常用卫星降水产品一览
Table 1 List of common satellite precipitation products
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 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)
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
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
表4 降水观测数据源差别统计表
Table 4 Statistics of different precipitation observation data sources
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
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