Neural network retrieval of ocean surface parameters from SSM/I data

doi:10.1175/MWR3292.1

IOCAS-IR > 海洋环流与波动重点实验室

	Neural network retrieval of ocean surface parameters from SSM/I data
	Meng, Lei; He, Yijun; Chen, Jinnian; Wu, Yumei; He, YJ, Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China
	2007-02-01
发表期刊	MONTHLY WEATHER REVIEW
ISSN	0027-0644
卷号	135 期号:2 页码:586-597
文章类型	Article
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.
关键词	Sensor Microwave Imager Air-sea Fluxes Algorithm
学科领域	Meteorology & Atmospheric Sciences
DOI	10.1175/MWR3292.1
URL	查看原文
收录类别	SCI
语种	英语
WOS记录号	WOS:000244102400019
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/1761
专题	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.

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