A new model to estimate significant wave heights with ERS- 1/2 scatterometer data

• Published in: Chinese journal of oceanology and limnology Vol. 27; no. 1; pp. 112 - 116
• Main Author: 过杰 何宜军 William Perrie 申辉 储小青
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science Press 01.02.2009; Springer Nature B.V; Institute of Oceanology, Chinese Academy of Sciences, Key Laboratory of Ocean Circulation and Wave, CAS, Qingdao 266071, China; Graduate School of the Chinese Academy of Sciences, Beijing 100039, China%Institute of Oceanology, Chinese Academy of Sciences, Key Laboratory of Ocean Circulation and Wave, CAS, Qingdao 266071, China%Bedford Institute of Oceanography, Dartmouth, NS, Canada
• Subjects: Algorithms; Backscatter; Backscattering; Earth and Environmental Science; Earth Sciences; ERS; Marine; Neural networks; Oceanography; Radar; Radar cross sections; Scattering cross sections; Scatterometers; Significant wave height; Wave height; Wave parameters; Wave period; 有效波高; 波参数; 神经网络算法; 雷达散射截面; wind waves; significant wave height; swell; scatterometer; neural networks
• ISSN: 0254-4059; 2096-5508; 1993-5005; 1993-5005; 2523-3521
• DOI: 10.1007/s00343-009-0112-1

A new model is proposed to estimate the significant wave heights with ERS-1/2 scatterometer data. The results show that the relationship between wave parameters and radar backscattering cross section is similar to that between wind and the radar backscattering cross section. Therefore, the relationship between significant wave height and the radar backscattering cross section is established with a neural network algorithm, which is, if the average wave period is 〈7s, the root mean square of significant wave height retrieved from ERS- 1/2 data is 0.51 m, or 0.72 m if it is 〉7s otherwise.