Shi et al. (2025) Towards an easy-to-use algorithm to estimate longwave cloud radiative forcing: algorithm development and preliminary evaluation

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-11-04
• Authors: Chuanye Shi, Tianxing Wang, Zheng Li, Dahui Li, Husi Letu
• DOI: 10.1016/j.rse.2025.115107

Research Groups

• School of Geospatial Engineering and Science, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China
• Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, China
• Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of natural resources, China
• Aerospace Information Research Institute, Chinese Academy of Sciences, China

Short Summary

This study develops a lightweight algorithm to estimate surface longwave cloud radiative forcing (LWCRF) using only five readily available parameters, achieving a theoretical root-mean-square error (RMSE) of 5.47 W/m² and an RMSE of 12.03 W/m² against global satellite data.

Objective

• To develop an easy-to-use, lightweight algorithm for estimating surface longwave cloud radiative forcing (LWCRF) using a minimal set of readily available parameters.
• To evaluate the accuracy and consistency of the developed algorithm against rigorous radiative transfer simulations and global satellite observations.

Study Configuration

• Spatial Scale: Global (evaluated using 812 million global samples).
• Temporal Scale: Not explicitly defined for the 812 million samples, but based on CERES CRS data, which provides continuous records.

Methodology and Data

• Models used:
  • A newly developed lightweight algorithm for LWCRF estimation.
  • Radiative transfer simulations (for theoretical testing).
  • Langley Fu-Liou radiative transfer simulations (underlying CERES CRS data).
• Data sources:
  • Digital elevation model
  • Column water vapor
  • Cloud top temperature
  • Cloud optical thickness
  • Cloud fraction ratio
  • CERES CRS (Clouds and the Earth’s Radiant Energy System, Clouds and Radiative Swath) data

Main Results

• A lightweight algorithm was developed to estimate surface LWCRF using five readily available parameters: digital elevation model, column water vapor, cloud top temperature, cloud optical thickness, and cloud fraction ratio.
• The algorithm demonstrated a theoretical root-mean-square error (RMSE) of 5.47 W/m² when tested against rigorous radiative transfer simulations.
• When applied to 812 million global samples from CERES CRS data, the algorithm exhibited an RMSE of 12.03 W/m², showing strong consistency in spatial patterns.

Contributions

• Provides a practical and efficient alternative for estimating surface LWCRF, particularly when the numerous inputs required by complex radiative transfer models are unavailable.
• Facilitates easier assessment of the impact of clouds on the global radiation balance.
• Advances the understanding of the extent to which clouds contribute to the Earth's warming or cooling.

Funding

• No funding information was provided in the article text.

Citation

@article{Shi2025Towards,
  author = {Shi, Chuanye and Wang, Tianxing and Li, Zheng and Li, Dahui and Letu, Husi},
  title = {Towards an easy-to-use algorithm to estimate longwave cloud radiative forcing: algorithm development and preliminary evaluation},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115107},
  url = {https://doi.org/10.1016/j.rse.2025.115107}
}