Lang et al. (2025) Significant uncertainties from overlooking aerosol-cloud coexistence in surface solar radiation estimates using passive satellite observations

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-12-02
• Authors: Qin Lang, Ming Zhang, Qiuhua He, Shikuan Jin, Wenmin Qin, Lingxia Luo, Lunche Wang
• DOI: 10.1016/j.rse.2025.115168

Research Groups

• School of Future Technology, China University of Geosciences
• Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences
• Hunan Provincial Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area, Hunan Center of Natural Resources Affairs
• State Key Laboratory of Geomicrobiology and Environmental Changes, China University of Geosciences

Short Summary

This study systematically evaluates the significant uncertainties introduced by overlooking aerosol-cloud coexistence in surface solar radiation estimates derived from passive satellite observations. It finds that ignoring this coexistence leads to substantial errors in direct and diffuse solar radiation, highlighting the need for improved methodologies.

Objective

• To systematically evaluate the impact of overlooking aerosol-cloud coexistence on estimates of surface direct solar radiation (DIR), diffuse solar radiation (DIF), and total solar radiation (TSR) using radiative transfer simulations, multi-source satellite data, and ground-based validation.

Study Configuration

• Spatial Scale: Global, with specific analysis of coexistence hotspots in central Africa.
• Temporal Scale: Not explicitly stated for the data period.

Methodology and Data

• Models used: Radiative transfer model (for simulations).
• Data sources: Multi-source passive satellite data (AQUA, Himawari-8), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), MERRA-2 reanalysis, MYD08M3 product, and ground-based validation data.

Main Results

• Radiative transfer simulations show that as cloud optical thickness increases from 0.2 to 5, the mean DIR overestimation due to ignoring aerosols decreases from 261.58 W m−2 to 1.02 W m−2. DIF shifts from an underestimation of 165.75 W m−2 to an overestimation of 102.41 W m−2. Ignoring clouds in coexistence cases yields similar patterns but with greater biases.
• CALIPSO data indicate that approximately 15 % of global samples exhibit aerosol-cloud coexistence, with concentrations in central Africa.
• Ground validation reveals that ignoring coexistence increases the relative root mean square error (rRMSE) of AQUA-based DIR, DIF, and TSR estimates by 19.19 %, 26.22 %, and 1.95 %, respectively. For Himawari-8-based estimates, the rRMSE increases by 31.21 %, 30.09 %, and 3.60 %, respectively.
• Mitigation strategies: Filling missing aerosol or cloud properties using MERRA-2 or MYD08M3 data reduces DIF rRMSE by 7.17 %–10.45 %. Incorporating aerosol-cloud coexistence samples into the lookup table reduces DIF rRMSE by 3.30 %. Both strategies have minimal impact on DIR and TSR.

Contributions

• Provides a systematic and quantitative evaluation of the uncertainties introduced by overlooking aerosol-cloud coexistence in passive satellite-based surface solar radiation estimates.
• Highlights the significant impact of this oversight, particularly on direct and diffuse radiation components.
• Identifies global hotspots of aerosol-cloud coexistence using CALIPSO data.
• Proposes and tests two strategies to mitigate these uncertainties, offering practical improvements for future methodologies.
• Emphasizes the critical need for greater awareness and methodological refinement in satellite-based solar radiation retrieval algorithms.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Lang2025Significant,
  author = {Lang, Qin and Zhang, Ming and He, Qiuhua and Jin, Shikuan and Qin, Wenmin and Luo, Lingxia and Wang, Lunche},
  title = {Significant uncertainties from overlooking aerosol-cloud coexistence in surface solar radiation estimates using passive satellite observations},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115168},
  url = {https://doi.org/10.1016/j.rse.2025.115168}
}