Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

Yili Zhao

doi:10.1007/s00343-013-2079-1

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Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

OriginalPaper | Updated：2023-05-16

- Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model
- Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model
- 海洋湖沼学报（英文） 2013年31卷第1期页码：210-218
- Affiliations：
  
  1. College of Information Science and Engineering Ocean University of China
  2. Department of Remote Sensing and Mapping National Ocean Technology Center
- Author bio：
- Funds：
- DOI：10.1007/s00343-013-2079-1
  中图分类号：
- 网络首发：2012-11-28，
  
  纸质出版：2013
- Accepted：
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Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model[J]. 海洋湖沼学报（英文）, 2013,31(1):210-218.

Zhao, Y. Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model., Chin. J. Ocean. Limnol. 31, 210–218 (2013). https://doi.org/10.1007/s00343-013-2079-1
Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model[J]. 海洋湖沼学报（英文）, 2013,31(1):210-218. DOI： 10.1007/s00343-013-2079-1.

Zhao, Y. Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model., Chin. J. Ocean. Limnol. 31, 210–218 (2013). https://doi.org/10.1007/s00343-013-2079-1 DOI：

摘要

Abstract

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer

which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper

a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation

by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC)

root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction

respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications

without reducing accuracy.

关键词

Keywords

references

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