Moradikian et al. (2025) Identifying and Characterizing Dust-Induced Cirrus Clouds by Synergic Use of Satellite Data

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-09-13
• Authors: Samaneh Moradikian, Sanaz Moghim, Gholam Ali Hoshyaripour
• DOI: 10.3390/rs17183176

Research Groups

• Department of Civil Engineering, Sharif University of Technology, Tehran, Iran
• Institute of Meteorology and Climate Research Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany

Short Summary

This study develops an algorithm to identify and characterize dust-induced cirrus clouds using synergic satellite data, revealing that these clouds are thicker, form at higher altitudes, and are more frequent in the Aral Sea and Iberian Peninsula regions, with significant seasonal and regional variations.

Objective

• To develop a systematic detection method for dust-induced cirrus clouds using long-term observational satellite data.
• To analyze the frequency and characterize the unique geometric and microphysical properties of dust-induced cirrus clouds compared to normal cirrus clouds.
• To provide a statistical basis for understanding how mineral dust influences cirrus cloud formation and frequency, with implications for climate modeling and weather forecasting.

Study Configuration

• Spatial Scale: Central Asia (Aral Sea region, approximately 4.9 x 10⁵ km²) and the Iberian Peninsula (approximately 10⁶ km²).
• Temporal Scale: Long-term observational data: mid-2006 to 2021 for the Aral Sea region; 2007 to 2009 for the Iberian Peninsula.

Methodology and Data

• Models used: No explicit atmospheric models were used for simulation; however, temperature profiles from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis dataset were incorporated into CALIPSO products. The core of the study is a novel algorithm developed by the authors to identify dust-induced cirrus clouds.
• Data sources:
  • CALIPSO (Cloud-Aerosol Lidar with Orthogonal Polarization) Level 2 lidar vertical feature mask (VFM) data product.
  • DARDAR-Nice (satellite retrievals of ice crystal number concentration profiles from lidar and radar observations).
  • MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) for atmospheric temperature profiles (integrated into CALIPSO products).
  • MODIS (Moderate Resolution Imaging Spectroradiometer) for cross-validation (cloud top temperature, cloud top height, aerosol optical depth).
  • VIIRS (Visible Infrared Imaging Radiometer Suite) for cross-validation (aerosol optical depth).
  • Jeggle et al. (2023) dataset for cirrus cloud events (used for cross-validation in the Iberian Peninsula).

Main Results

• An algorithm was successfully developed and applied to identify dust-induced cirrus clouds based on spatial and temporal proximity of dust and cirrus layers, validated with a 97% verification rate in the Aral Sea region.
• Dust-induced cirrus clouds are highly prevalent, accounting for approximately 65% of all cirrus events in both the Aral Sea (1787 out of 2708 valid cases) and the Iberian Peninsula (246 out of 392 valid cases).
• In the Aral Sea, dust-induced cirrus clouds peak in frequency during spring, correlating with high dust concentrations.
• Dust-induced cirrus clouds are geometrically distinct: they are thicker, form at higher altitudes, and exhibit a greater cloud cover fraction compared to normal cirrus clouds (all statistically significant with p < 0.001).
• Microphysical properties (ice water content, ice crystal number concentration >5 µm, and effective radius) showed no statistically significant differences between dust-induced and normal cirrus clouds (p > 0.05), despite some qualitative trends.
• A sensitivity analysis confirmed the robustness of the detection algorithm to variations in horizontal (1 km to 4 km) and vertical (0.6 km to 2 km) dust layer thresholds.

Contributions

• Development of a novel, systematic, and robust algorithm for identifying dust-induced cirrus clouds from long-term satellite observational data (CALIPSO, DARDAR-Nice).
• Provides a comprehensive statistical analysis of the frequency, seasonal variability, and regional differences of dust-induced cirrus clouds in key dust-affected regions (Aral Sea, Iberian Peninsula).
• Characterizes the distinct geometric properties (thickness, altitude, cloud cover fraction) of dust-induced cirrus clouds, demonstrating their statistically significant differences from normal cirrus clouds.
• Offers insights into the complex interactions between mineral dust and cloud microphysics, highlighting the need for integrating dust-cloud feedback mechanisms into global climate models and weather forecasting.

Funding

• Sharif University of Technology (SUT) through Grant QB020103.
• Project PermaStrom within the seventh Energieforschungsprogramm of the German Federal Ministry of Economic Affairs and Climate Action (BMWK) through Grant 03EI4010B.

Citation

@article{Moradikian2025Identifying,
  author = {Moradikian, Samaneh and Moghim, Sanaz and Hoshyaripour, Gholam Ali},
  title = {Identifying and Characterizing Dust-Induced Cirrus Clouds by Synergic Use of Satellite Data},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17183176},
  url = {https://doi.org/10.3390/rs17183176}
}