Articles | Volume 18, issue 13
https://doi.org/10.5194/amt-18-2877-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2877-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2025
Research article |
| 04 Jul 2025
| 04 Jul 2025
Development and comparison of empirical models for all-sky downward longwave radiation estimation at the ocean surface using long-term observations
Jianghai Peng
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281, USA
Bo Jiang
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Hui Liang
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Shaopeng Li
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Jiakun Han
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Thomas C. Ingalls
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281, USA
Jie Cheng
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Yunjun Yao
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Kun Jia
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Xiaotong Zhang
State Key Laboratory of Remote Sensing Science, jointly sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing, China
China and Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136, https://doi.org/10.5194/essd-2025-136, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper describes 1 km daily mean land surface sensible heat flux (H) and land surface – air temperature difference (Tsa) datasets on the global scale during 2000–2020. The datasets were developed using a data-driven approach and rigorously validated against in situ observations and existing H and Tsa datasets, demonstrating both high accuracy and exceptional spatial resolution.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
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Short summary
We developed a new method to estimate how much heat the ocean surface receives from the atmosphere, which is important for understanding weather and climate. Using over 30 years of data from buoys around the world, we created a model that outperforms existing ones, especially under cloudy skies. Our results show that including detailed cloud information improves accuracy, making this method useful for global climate and ocean research.
We developed a new method to estimate how much heat the ocean surface receives from the...
