A new algorithm for sea-surface wind-speed retrieval based on the L-band radiometer onboard Aquarius

被引:0
作者
Jin Wang
Jie Zhang
Chenqing Fan
Jing Wang
机构
[1] Ocean University of China,College of Information Science and Engineering
[2] State Oceanic Administration,First Institute of Oceanography
[3] Qingdao University,College of Physics
来源
Chinese Journal of Oceanology and Limnology | 2015年 / 33卷
关键词
microwave radiometer; Aquarius; wind speed; L-band;
D O I
暂无
中图分类号
学科分类号
摘要
Aquarius is the second satellite mission to focus on the remote sensing of sea-surface salinity from space and it has mapped global sea-surface salinity for nearly 3 years since its launch in 2011. However, benefiting from the high atmospheric transparency and moderate sensitivity to wind speed of the L-band brightness temperature (TB), the Aquarius L-band radiometer can actually provide a new technique for the remote sensing of wind speed. In this article, the sea-surface wind speeds derived from TBs measured by Aquarius’ L-band radiometer are presented, the algorithm for which is developed and validated using multisource wind speed data, including WindSat microwave radiometer and National Data Buoy Center buoy data, and the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory wind field product. The error analysis indicates that the performance of retrieval algorithm is good. The RMSE of the Aquarius wind-speed algorithm is about 1 and 1.5 m/s for global oceans and areas of tropical hurricanes, respectively. Consequently, the applicability of using the Aquarius L-band radiometer as a near all-weather wind-speed measuring method is verified.
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页码:1115 / 1123
页数:8
相关论文
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  • [1] Amarin R A(2012)Hurricane wind speed measurements in rainy conditions using the airborne hurricane imaging radiometer (HIRAD) IEEE Trans. GeoSci. Remote Sens. 50 180-192
  • [2] Jones W L(1978)Measurement of ocean temperature and salinity via microwave radiometry Boundary-Layer Meteorology 13 295-308
  • [3] El-Nimri S F(2013)Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain Ocean Sci. 9 183-192
  • [4] Blume H J C(2012)First assessment of SMOS data over open ocean: Part II—sea surface salinity IEEE Trans. GeoSci. Remote Sens. 50 1662-1675
  • [5] Kendall B M(1970)An airborne measurement of salinity variations of the Mississippi river overflow J. Geophys. Res. 78 5909-5013
  • [6] Fedors J C(2004)A new empirical model of sea surface microwave emissivity for salinity remote sensing Geophysical Research Letters 31 1-5
  • [7] Boutin J(2012)SMOS semi-empirical ocean forward model adjustment IEEE Trans. GeoSci. Remote Sens. 50 1676-1687
  • [8] Martin N(2012)Foam and roughness effects on passive microwave remote sensing of the ocean IEEE Trans. GeoSci. Remote Sens. 50 2978-2985
  • [9] Reverdin G(2006)An efficient two-scale model for the computation of thermal emission and atmospheric reflection from the sea surface IEEE Trans. GeoSci. Remote Sens. 44 560-568
  • [10] Boutin J(1977)An improved model for the dielectric constant of sea water at microwave IEEE Transactions On Frequencies, Antennas and Propagation 25 104-111