Editorial for Special Issue “Remote Sensing of Precipitation Extremes”
Journal
Remote Sensing
Date Issued
October 11, 2025
DOI
10.3390/rs17203406
Abstract
Recent years have seen tremendous advancements in the field of extreme precipitation monitoring, particularly through the application of remote sensing technologies. With the increasing frequency and intensity of hydro-meteorological extremes under a changing climate, there is an urgent need to strengthen our capacity to monitor, understand, and manage extreme rainfall and snowfall events globally. Satellite-based precipitation products, offering high spatial and temporal resolution, broad coverage, and near-real-time availability, are playing an increasingly central role in addressing gaps left by traditional in situ observation networks, especially in data-sparse regions.
Despite significant progress, several critical challenges remain. These include calibration and integration of different data sources, improving the accuracy of quantitative precipitation estimation (QPE), especially for extremes, and understanding the complex processes underlying extreme precipitation in diverse climatic and topographic settings. Additionally, translating satellite-based precipitation information into actionable knowledge for hydrological applications and disaster risk management is still an ongoing challenge. The use of advanced data fusion techniques, machine learning algorithms, and integrated approaches to downscaling and bias adjustment have shown promise, but further development is needed for reliable operational applications.
This Special Issue hosts nine papers devoted to remote sensing applications in precipitation, bringing together a diverse collection of studies that address many of the above challenges. These contributions span a range of topics, including the following:
Calibration and evaluation of ground-based radar networks using satellite data;
Fusion of radar and gauge data to enhance QPE reliability;
Performance assessment and bias characterization of satellite precipitation products such as GPM IMERG and TRMM in various climatic regions, including complex terrain;
Analysis of extreme precipitation events in the context of floods, droughts, and tropical cyclones;
Case studies on precipitation monitoring in areas with limited in situ data.
Section 2 summarizes the individual articles hosted in this Special Issue in alphabetical order based on the first author’s name, and Section 3 outlines some concluding remarks.
Despite significant progress, several critical challenges remain. These include calibration and integration of different data sources, improving the accuracy of quantitative precipitation estimation (QPE), especially for extremes, and understanding the complex processes underlying extreme precipitation in diverse climatic and topographic settings. Additionally, translating satellite-based precipitation information into actionable knowledge for hydrological applications and disaster risk management is still an ongoing challenge. The use of advanced data fusion techniques, machine learning algorithms, and integrated approaches to downscaling and bias adjustment have shown promise, but further development is needed for reliable operational applications.
This Special Issue hosts nine papers devoted to remote sensing applications in precipitation, bringing together a diverse collection of studies that address many of the above challenges. These contributions span a range of topics, including the following:
Calibration and evaluation of ground-based radar networks using satellite data;
Fusion of radar and gauge data to enhance QPE reliability;
Performance assessment and bias characterization of satellite precipitation products such as GPM IMERG and TRMM in various climatic regions, including complex terrain;
Analysis of extreme precipitation events in the context of floods, droughts, and tropical cyclones;
Case studies on precipitation monitoring in areas with limited in situ data.
Section 2 summarizes the individual articles hosted in this Special Issue in alphabetical order based on the first author’s name, and Section 3 outlines some concluding remarks.
Subjects
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