Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

Panpan Yao; Jianhua Wan; Jin Wang; Jie Zhang

doi:10.1007/s00343-015-4131-9

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Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

Updated：2023-05-16

- Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer
- Journal of Oceanology and Limnology Vol. 33, Issue 5, Pages: 1104-1114(2015)
- Affiliations：
  
  1. State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences
  2. First Institute of Oceanography State Oceanic Administration
  3. School of Geosciences China University of Petroleum
  4. College of Physics Qingdao University
- Author bio：
- Funds：
- DOI：10.1007/s00343-015-4131-9
  CLC：
- Online First：08 January 2015，
  
  Published：2015
- Accepted：
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Yao, P., Wan, J., Wang, J. et al. Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer., Chin. J. Ocean. Limnol. 33, 1104–1114 (2015). https://doi.org/10.1007/s00343-015-4131-9
DOI：

Yao, P., Wan, J., Wang, J. et al. Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer., Chin. J. Ocean. Limnol. 33, 1104–1114 (2015). https://doi.org/10.1007/s00343-015-4131-9 DOI：

摘要

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.

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references

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