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  4. A review of the past half century of geostationary satellite thermal observations for global precipitation estimation: developments, achievements, and future prospects
 
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A review of the past half century of geostationary satellite thermal observations for global precipitation estimation: developments, achievements, and future prospects

Publikationstyp
Review Article
Date Issued
2026-03-18
Sprache
English
Author(s)
Zhu, Siyu  
Tang, Guoqiang  
Sorooshian, Soroosh  
Huffman, George J.  
Duan, Qingyun  
Yan, Songkun  
Behrangi, Ali  
Papalexiou, Simon Michael  
Global Water Security B-2  
Nguyen, Phu  
Hsu, Kuolin  
Wen, Yixin  
Liu, Zhong  
Li, Zhi  
Chen, Mengye  
Laviola, Sante  
Hong, Yang  
TORE-URI
https://hdl.handle.net/11420/62543
Journal
IEEE geoscience and remote sensing magazine  
Start Page
2
End Page
25
Citation
IEEE geoscience and remote sensing magazine (in Press): 2-25 (2026)
Publisher DOI
10.1109/MGRS.2026.3665828
Scopus ID
2-s2.0-105033381418
Publisher
IEEE Geoscience and Remote Sensing Society
Over the past half century since 1975, the development and improvement of meteorological geostationary (GEO) satellites have played a pivotal role in observing cloud dynamics and subsequently in advancing spaceborne precipitation estimation. Infrared (IR) observation from GEO satellites offers unique advantages, such as broad spatial coverage, high temporal resolution, and long-term consistency, motivating extensive research to unlock the potential of GEO IR-based data for capturing precipitation structure and dynamics. This article reviews the major developments and milestone achievements of GEO IR-based precipitation estimation over the past five decades and summarizes the future prospects, including potential directions and remaining challenges. By examining the history of global GEO satellites and more than 100 references in this domain, we categorize the development of GEO IR-based precipitation estimation methodology and technology into three distinct stages: 1) the Exploration Phase (1975 to ca. 1995), 2) the Growth Phase (ca. 1995 to ca. 2015), and 3) the Exploitation Phase (ca. 2015 to the present). As we transition into an emerging new stage, these efforts collectively point toward multispectral retrievals, lifecycle-aware machine learning (ML), smart sensing, and advanced multisource integration as key directions shaping the future of GEO IR-based precipitation estimation. In summary, GEO IR-based precipitation estimation has made substantial contributions over the past half century and will play an increasingly important role with great potential in future precipitation science.
DDC Class
600: Technology
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