Neural network retrieval of ocean surface parameters from SSM/I data
文献类型：期刊论文
| 作者 | Meng, Lei; He, Yijun; Chen, Jinnian; Wu, Yumei; He, YJ, Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China |
| 刊名 | MONTHLY WEATHER REVIEW
 |
| 出版日期 | 2007-02-01 |
| 卷号 | 135期号:2页码:586-597 |
| 关键词 | Sensor Microwave Imager Air-sea Fluxes Algorithm |
| ISSN号 | 0027-0644 |
| DOI | 10.1175/MWR3292.1 |
| 文献子类 | Article |
| 英文摘要 | A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data.; A new algorithm based on the multiparameter neural network is proposed to retrieve wind speed (WS), sea surface temperature (SST), sea surface air temperature, and relative humidity ( RH) simultaneously over the global oceans from Special Sensor Microwave Imager (SSM/I) observations. The retrieved geophysical parameters are used to estimate the surface latent heat flux and sensible heat flux using a bulk method over the global oceans. The neural network is trained and validated with the matchups of SSM/I overpasses and National Data Buoy Center buoys under both clear and cloudy weather conditions. In addition, the data acquired by the 85.5-GHz channels of SSM/I are used as the input variables of the neural network to improve its performance. The root-mean-square (rms) errors between the estimated WS, SST, sea surface air temperature, and RH from SSM/I observations and the buoy measurements are 1.48 m s(-1), 1.54 degrees C, 1.47 degrees C, and 7.85, respectively. The rms errors between the estimated latent and sensible heat fluxes from SSM/I observations and the Xisha Island ( in the South China Sea) measurements are 3.21 and 30.54 W m(-2), whereas those between the SSM/ I estimates and the buoy data are 4.9 and 37.85 W m(-2), respectively. Both of these errors ( those for WS, SST, and sea surface air temperature, in particular) are smaller than those by previous retrieval algorithms of SSM/ I observations over the global oceans. Unlike previous methods, the present algorithm is capable of producing near-real-time estimates of surface latent and sensible heat fluxes for the global oceans from SSM/I data. |
| 学科主题 | Meteorology & Atmospheric Sciences |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| WOS记录号 | WOS:000244102400019 |
| 公开日期 | 2010-11-18 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/1762]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | He, YJ, Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China |
| 作者单位 | Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Meng, Lei,He, Yijun,Chen, Jinnian,et al. Neural network retrieval of ocean surface parameters from SSM/I data[J]. MONTHLY WEATHER REVIEW,2007,135(2):586-597. |
| APA | Meng, Lei,He, Yijun,Chen, Jinnian,Wu, Yumei,&He, YJ, Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China.(2007).Neural network retrieval of ocean surface parameters from SSM/I data.MONTHLY WEATHER REVIEW,135(2),586-597. |
| MLA | Meng, Lei,et al."Neural network retrieval of ocean surface parameters from SSM/I data".MONTHLY WEATHER REVIEW 135.2(2007):586-597. |
入库方式： OAI收割
来源：海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。