Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer -- Journal of Oceanology and Limnology,2015,33(5):1104-1114

	Cite this paper:
	YAO Panpan, WAN Jianhua, WANG Jin, ZHANG Jie. Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer[J]. Journal of Oceanology and Limnology, 2015, 33(5): 1104-1114

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

YAO Panpan^1,3,4, WAN Jianhua⁴, WANG Jin², ZHANG Jie³

1 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
2 College of Physics, Qingdao University, Qingdao 266071, China;
3 First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China;
4 School of Geosciences, China University of Petroleum, Qingdao 266580, China

Abstract:

The AMSR2 microwave radiometer is the main payload of the GCOM-W1 satellite, launched by the Japan Aerospace Exploration Agency in 2012.Based on the pre-launch information extraction algorithm, the AMSR2 enables remote monitoring of geophysical parameters such as sea surface temperature, wind speed, water vapor, and liquid cloud water content.However, rain alters the properties of atmospheric scattering and absorption, which contaminates the brightness temperatures measured by the microwave radiometer.Therefore, it is difficult to retrieve AMSR2-derived sea surface wind speeds under rainfall conditions.Based on microwave radiative transfer theory, and using AMSR2 L1 brightness temperature data obtained in August 2012 and NCEP reanalysis data, we studied the sensitivity of AMSR2 brightness temperatures to rain and wind speed, from which a channel combination of brightness temperature was established that is insensitive to rainfall, but sensitive to wind speed.Using brightness temperatures obtained with the proposed channel combination as input parameters, in conjunction with HRD wind field data, and adopting multiple linear regression and BP neural network methods, we established an algorithm for hurricane wind speed retrieval under rainfall conditions.The results showed that the standard deviation and relative error of retrievals, obtained using the multiple linear regression algorithm, were 3.1 m/s and 13%, respectively.However, the standard deviation and relative error of retrievals obtained using the BP neural network algorithm were better (2.1 m/s and 8%, respectively).Thus, the results of this paper preliminarily verified the feasibility of using microwave radiometers to extract sea surface wind speeds under rainfall conditions.

Key words: microwave radiometer|AMSR2|sea surface wind speeds|hurricane

Received: 2014-04-30 Revised: 2014-07-07

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Articles by YAO Panpan

Articles by WAN Jianhua

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Articles by ZHANG Jie

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