Review of post-processing research for remote-sensing precipitation products

被引：0
|
作者：
Xiong L. [1 ]
Liu C. [1 ]
Chen S. [2 ]
Zha X. [1 ]
Ma Q. [3 ]
机构：
[1] State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan
[2] Changjiang Institute of Survey, Planning, Design and Research, Wuhan
[3] Renewable Energy School, North China Electric Power University, Beijing
来源：
| 1600年 / International Research and Training Center on Erosion and Sedimentation and China Water and Power Press卷 / 32期
基金：
中国国家自然科学基金;
关键词：
Bias correction; Evaluation indices; Multi-product fusion; Rain gauge; Remote-sensing precipitation; Spatial downscaling;
D O I：
10.14042/j.cnki.32.1309.2021.04.014
中图分类号：
学科分类号：
摘要：
Obtaining high-precision, high-resolution precipitation data is of great significance for hydrological analysis, water resources management, and flood and drought monitoring. Although remote-sensing precipitation products can effectively reproduce the spatial and temporal distribution of precipitation, few original remote-sensing precipitation products can meet the requirements in either precision or resolution in the hydrological field. It is therefore necessary to carry out post-processing research on existing remote-sensing precipitation products. The main methods used to obtain precipitation data are introduced, including rain-gauge observations, weather radar estimates, and satellite products. The advantages of each method and their problems are discussed. Next, the research advances made in post-processing methods of remote-sensing precipitation products are summarized, including spatial downscaling, bias correction, and multi-product fusion. Then, the indices used for evaluation of post-processing precipitation products are reviewed. Finally, the following research directions that must be pursued more avidly in the future are discussed:development and improvement of precipitation-estimation techniques, construction of a more reasonable framework for multi-source precipitation data fusion, strengthening of the comparative study of downscaling methods and ideas for their improvement, and uncertainty analysis. © 2021, Editorial Board of Advances in Water Science. All right reserved.
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收藏
页码：627 / 637
页数：10
相关论文
共 57 条
  • [1] YUAN D B, AI P, HONG M, Et al., Estimation of areal rainfall in Yalong River basin based on geospatial factors, Advances in Water Science, 29, 6, pp. 779-787, (2018)
  • [2] REN G Y, ZHAN Y J, REN Y Y, Et al., Spatial and temporal patterns of precipitation variability over mainland China:I:climatology, Advances in Water Science, 26, 3, pp. 299-310, (2015)
  • [3] WU B F, ZHU W W, ZENG H W, Et al., Watershed remote sensing:definition and prospective, Advances in Water Science, 31, 5, pp. 654-673, (2020)
  • [4] TIAN D D, YIN S Q., Advances in radar rainfall measurement and its application in soil erosion, Journal of Soil and Water Conservation, 33, 6, pp. 1-9, (2019)
  • [5] SHARIFI E, SAGHAFIAN B, STEINACKER R., Downscaling satellite precipitation estimates with multiple linear regression, artificial neural networks, and spline interpolation techniques, Journal of Geophysical Research:Atmospheres, 124, 2, pp. 789-805, (2019)
  • [6] SUN L Q, HAO Z C, WANG J H, Et al., Assessment and correction of TMPA products 3B42RT and 3B42V6, Journal of Hydraulic Engineering, 45, 10, pp. 1135-1146, (2014)
  • [7] HE H., China gauging station network, Advances in Water Science, 21, 4, pp. 460-465, (2010)
  • [8] YAN J R, BARDOSSY A., Short time precipitation estimation using weather radar and surface observations:with rainfall displacement information integrated in a stochastic manner[J], Journal of Hydrology, 574, pp. 672-682, (2019)
  • [9] TAPIADOR F J, TURK F J, PETERSEN W, Et al., Global precipitation measurement:methods, datasets and applications[J], Atmospheric Research, 104, 105, pp. 70-97, (2012)
  • [10] CHEN H, HUO R, ZENG Q, Et al., Comparative study on the influence of rain-gauge network on the uncertainty of hydrological modeling, Advances in Water Science, 30, 1, pp. 34-44, (2019)
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