Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval
The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2020.552833/full |
| _version_ | 1818254367531204608 |
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| author | Wei Zhang Wei Zhang Zhuhui Jiang Jie Xiang Hanqing Shi |
| author_facet | Wei Zhang Wei Zhang Zhuhui Jiang Jie Xiang Hanqing Shi |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this article, we propose a new wind retrieval method by applying the optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated well-characterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. The results from the simulation data show that the error of the OI-retrieved wind is smaller than that of background wind in all considered cases; in particular, the accuracy of the OI-retrieved wind speed is significantly improved. Experiments on the Sentinel-1 SAR data show that the root mean square error of the OI-retrieved wind speed and direction are 1.4 m/s and 35°, respectively. Compared with other methods, the retrieved wind speed accuracy of the OI method is similar to that of the VAR method but higher than that of the direct wind retrieval method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The results indicate that the OI method can be effectively applied to SAR wind retrieval. |
| first_indexed | 2024-12-12T16:54:50Z |
| format | Article |
| id | doaj.art-4b29a3f2d399489abd330bc990846a8d |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-12T16:54:50Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-4b29a3f2d399489abd330bc990846a8d2022-12-22T00:18:15ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632020-10-01810.3389/feart.2020.552833552833Optimal Interpolation Model for Synthetic Aperture Radar Wind RetrievalWei Zhang0Wei Zhang1Zhuhui Jiang2Jie Xiang3Hanqing Shi4College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, ChinaChina Satellite Maritime Tracking and Controlling Department, Jiangyin, ChinaState Key Laboratory of Geo-Information Engineering, Xi’an, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing, ChinaThe variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this article, we propose a new wind retrieval method by applying the optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated well-characterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. The results from the simulation data show that the error of the OI-retrieved wind is smaller than that of background wind in all considered cases; in particular, the accuracy of the OI-retrieved wind speed is significantly improved. Experiments on the Sentinel-1 SAR data show that the root mean square error of the OI-retrieved wind speed and direction are 1.4 m/s and 35°, respectively. Compared with other methods, the retrieved wind speed accuracy of the OI method is similar to that of the VAR method but higher than that of the direct wind retrieval method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The results indicate that the OI method can be effectively applied to SAR wind retrieval.https://www.frontiersin.org/articles/10.3389/feart.2020.552833/fullsynthetic aperture radarsea surface windoptimal interpolationvariational model inversionC-band model direct wind retrieval |
| spellingShingle | Wei Zhang Wei Zhang Zhuhui Jiang Jie Xiang Hanqing Shi Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval Frontiers in Earth Science synthetic aperture radar sea surface wind optimal interpolation variational model inversion C-band model direct wind retrieval |
| title | Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval |
| title_full | Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval |
| title_fullStr | Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval |
| title_full_unstemmed | Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval |
| title_short | Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval |
| title_sort | optimal interpolation model for synthetic aperture radar wind retrieval |
| topic | synthetic aperture radar sea surface wind optimal interpolation variational model inversion C-band model direct wind retrieval |
| url | https://www.frontiersin.org/articles/10.3389/feart.2020.552833/full |
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