Back to Search Start Over

A New Empirical Estimation Scheme for Daily Net Radiation at the Ocean Surface

Authors :
Jianghai Peng
Bo Jiang
Hongkai Chen
Shunlin Liang
Hui Liang
Shaopeng Li
Jiakun Han
Qiang Liu
Jie Cheng
Yunjun Yao
Kun Jia
Xiaotong Zhang
Source :
Remote Sensing, Vol 13, Iss 20, p 4170 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Ocean surface net radiation (Rn) is significant in research on the Earth’s heat balance systems, air–sea interactions, and other applications. However, there have been few studies on Rn until now. Based on radiative and meteorological measurements collected from 66 globally distributed moored buoys, it was found that Rn was dominated by downward shortwave radiation (Rg↓) when the length ratio of daytime (LRD) was greater than 0.4 but dominated by downward longwave radiation (Rl↓) for the other cases (LRD ≤ 0.4). Therefore, an empirical scheme that includes two conditional models named Case 1 (LRD > 0.4) utilizing Rg↓ as a major input and Case 2 (LRD ≤ 0.4) utilizing Rl↓ as a major input for Rn estimation was successfully developed. After validation against in situ Rn, the performance of the empirical scheme was satisfactory with an overall R2 value of 0.972, an RMSE of 9.768 Wm−2, and a bias of −0.092 Wm−2. Specifically, the accuracies of the two conditional models were also very good, with RMSEs of 9.805 and 2.824 Wm−2 and biases of −0.095 and 0.346 Wm−2 for the Case 1 and Case 2 models, respectively. However, due to the limited number of available samples, the performances of these new models were poor in coastal and high-latitude areas, and the models did not work when the LRD was too small (i.e., LRD < 0.3). Overall, the newly developed empirical scheme for Rn estimation has strong potential to be widely used in practical use because of its simple format and high accuracy.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.6d4a15c0bae34ac380ce29f1edcdda66
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13204170