Review on spatial downscaling of satellite derived precipitation estimates

被引:11
|
作者
Kofidou, Maria [1 ]
Stathopoulos, Stavros [1 ]
Gemitzi, Alexandra [1 ]
机构
[1] Democritus Univ Thrace, Dept Environm Engn, Xanthi, Greece
关键词
GPM IMERG; GPM precipitation; machine learning; precipitation downscaling; Satellite-derived precipitation; TRMM; LAND-SURFACE CHARACTERISTICS; REMOTELY-SENSED RAINFALL; GLOBAL PRECIPITATION; DATA ASSIMILATION; UNIVERSAL MULTIFRACTALS; PROFILING ALGORITHM; MESOSCALE RAINFALL; TRMM SATELLITE; MAINLAND CHINA; SOIL-MOISTURE;
D O I
10.1007/s12665-023-11115-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The present work aims at reviewing and identifying gaps in knowledge and future perspectives of satellite-derived precipi-tation downscaling algorithms. Here, various aspects related to statistical and dynamical downscaling approaches of the precipitation data sets from the Tropical Rainfall Measuring Mission (TRMM) and its successor Intergraded Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) mission are reviewed and the existing downscaling methods are categorized and analysed, to highlight the usefulness and applicability of the produced downscaled precipitation data sets. In addition, a critical comparison of the various statistical and dynamical methods for spatial or spatiotemporal downscaling of GPM and TRMM precipitation estimates was conducted, in terms of their advantages and disadvantages, simplicity of application and their suitability at different regional and temporal scales and hydroclimatic conditions. Finally, the adequacy of downscaling remotely sensed precipitation estimates as an effective way to obtain precipitation with sufficient spatial and temporal resolution is discussed and future challenges are highlighted.
引用
收藏
页数:33
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