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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

  • Siyu Zhu
  • , Guoqiang Tang
  • , Soroosh Sorooshian
  • , George J. Huffman
  • , Qingyun Duan
  • , Songkun Yan
  • , Ali Behrangi
  • , Simon Michael Papalexiou
  • , Phu Nguyen
  • , Kuolin Hsu
  • , Yixin Wen
  • , Zhong Liu
  • , Zhi Li
  • , Mengye Chen
  • , Sante Laviola
  • , Yang Hong
  • University of Oklahoma
  • Wuhan University
  • University of California
  • NASA Goddard Space Flight Center
  • Hohai University
  • University of Arizona
  • Hamburg University of Technology
  • University of Florida
  • George Mason University
  • University of Colorado Boulder
  • National Research Council of Italy

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)369-391
Number of pages23
JournalIEEE Geoscience and Remote Sensing Magazine
Volume14
Issue number3
DOIs
StatePublished - Jun 1 2026
Externally publishedYes

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