Sharifi et al. (2025) Editorial for Special Issue “Remote Sensing of Precipitation Extremes”

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-10-11
• Authors: Ehsan Sharifi, Silas Michaelides, Vincenzo Levizzani
• DOI: 10.3390/rs17203406

Research Groups

• Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
• GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
• Eratosthenes Centre of Excellence, Limassol, Cyprus
• Institute of Atmospheric Sciences and Climate, National Research Council of Italy, Bologna, Italy

Short Summary

This editorial introduces a special issue on "Remote Sensing of Precipitation Extremes," synthesizing nine research papers to highlight advancements, persistent challenges in monitoring and understanding extreme rainfall and snowfall events globally, and outlining future research directions.

Objective

• To introduce and synthesize the contributions of a special issue on "Remote Sensing of Precipitation Extremes," reviewing the current state of the field, identifying key challenges, and proposing future research directions for monitoring, understanding, and managing extreme precipitation events.

Study Configuration

• Spatial Scale: Global, with specific studies focusing on regions such as Tuscany (Italy), southwestern Iran, the southern Korean Peninsula, Cyprus, Henan Province (China), the Western Mediterranean, Punjab (Pakistan), coastal China, and the Qinghai–Tibet Plateau (China).
• Temporal Scale: Varies from sub-daily (e.g., half-hourly, hourly) to daily, seasonal, and multi-year periods (e.g., 10 years, 12 years, 15 years, 20 years, 40 years).

Methodology and Data

• Models used: Data fusion techniques, Global Flood Monitoring System (GFMS) model, statistical approaches (e.g., non-asymptotic), and the application of machine learning/AI algorithms are discussed as current and future methods.
• Data sources:
  • Satellite-based precipitation products (e.g., GPM IMERG (Early, Late, Final Run), TRMM (TMPA, 3B42V7, 3B42RT), CMORPH, PERSIANN-CDR, GSMAP)
  • Ground-based radar networks (e.g., X-band ground radars)
  • Rain gauge data (dense networks, rainfall stations)
  • Reanalysis products (e.g., ERA5)
  • Rawinsonde data
  • Global Navigation Satellite System (GNSS) datasets (from mobile observation vehicles)
  • Cloud Microphysics (CMIC–NWC SAF) data
  • Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products (Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST))

Main Results

• Significant advancements have been made in remote sensing of extreme precipitation, particularly through multi-source data fusion and high-resolution satellite products.
• Challenges persist in accurately capturing precipitation extremes in regions with complex topography, diverse climate regimes, or sparse ground-truth data.
• Merging radar and rain gauge data consistently outperforms individual approaches, reducing errors and improving Quantitative Precipitation Estimation (QPE) reliability.
• GPM IMERG-Final generally performs well in capturing extreme precipitation events and indices, especially for precipitation amount and intensity, but can exhibit biases (e.g., underestimation with increasing intensity, overestimation over inland water bodies, issues in elevated areas).
• Water vapor transport (e.g., precipitable water vapor exceeding 60 mm) and translation speed (e.g., 21.38 km/h vs. 17.87 km/h for tropical cyclones) are critical factors influencing heavy rainfall events, particularly in tropical cyclones.
• The performance of satellite products varies geographically (e.g., better retrieval in eastern and southern Qinghai–Tibet Plateau, lower accuracy in high-altitude and arid areas) and seasonally (e.g., better performance during summer and autumn).
• Future research directions emphasize enhanced multi-source data fusion, broader application of machine learning and AI for QPE and forecasting, improved extreme event characterization, operational implementation of research advances, refinement of bias adjustment and downscaling techniques, and closer stakeholder integration.

Contributions

• Provides a comprehensive overview of the state-of-the-art in remote sensing of precipitation extremes, synthesizing findings from nine diverse research papers.
• Highlights the current progress, persistent challenges, and critical gaps in monitoring and understanding extreme rainfall and snowfall events globally.
• Outlines key future research directions, including enhanced data fusion, broader application of AI/machine learning, improved extreme event characterization, operational implementation, bias adjustment/downscaling, and stakeholder integration.
• Emphasizes the importance of interdisciplinary collaboration and bridging the gap between remote sensing science and practical water management for hydrological forecasting and disaster risk reduction.

Funding

This research received no external funding.

Citation

@article{Sharifi2025Editorial,
  author = {Sharifi, Ehsan and Michaelides, Silas and Levizzani, Vincenzo},
  title = {Editorial for Special Issue “Remote Sensing of Precipitation Extremes”},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17203406},
  url = {https://doi.org/10.3390/rs17203406}
}