A damped Newton variational inversion method for SAR wind retrieval

Jiang, Zhuhui; Li, Yuanxiang; Yu, Fangjie; Chen, Ge; Wang, Yu

doi:10.1002/2016jd025178

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…At present, two methods are available for SSWF retrieval from SAR images using the CMOD: the direct retrieval method [ 25 , 26 ] and the variational method [ 10 , 27 , 28 , 29 ]. The direct retrieval method utilizes wind direction as prior information, which is input into the CMOD to obtain wind speed.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Sea-Surface Wind Retrieval from C-Band Miniaturized SAR Imagery

Yan

Xie

et al. 2023

Sensors

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Synthetic aperture radar (SAR) has been widely used for observing sea-surface wind fields (SSWFs), with many scholars having evaluated the performance of SAR in SSWF retrieval. Due to the large systems and high costs of traditional SAR, a tendency towards the development of smaller and more cost-effective SAR systems has emerged. However, to date, there has been no evaluation of the SSWF retrieval performance of miniaturized SAR systems. This study utilized 1053 HiSea-1 and Chaohu-1 miniaturized SAR images covering the Southeast China Sea to retrieve SSWFs. After a quality control procedure, the retrieved winds were subsequently compared with ERA5, buoy, and ASCAT data. The retrieved wind speeds demonstrated root mean square errors (RMSEs) of 2.42 m/s, 1.64 m/s, and 3.29 m/s, respectively, while the mean bias errors (MBEs) were found to be −0.44 m/s, 1.08 m/s, and −1.65 m/s, respectively. Furthermore, the retrieved wind directions exhibited RMSEs of 11.5°, 36.8°, and 41.7°, with corresponding MBEs of −1.3°, 2.4°, and −8.8°, respectively. The results indicate that HiSea-1 and Chaohu-1 SAR satellites have the potential and practicality for SSWF retrieval, validating the technical indicators and performance requirements implemented during the satellites’ design phase.

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Section: Introductionmentioning

confidence: 99%

Assessment of Sea-Surface Wind Retrieval from C-Band Miniaturized SAR Imagery

Yan

Xie

et al. 2023

Sensors

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“…However, since the variational formulation is a nonlinear equation, the analytical solution of the formulation cannot be acquired. Only the optimum solution can be acquired by the iterative or enumeration method, so the solution is unstable and the time latency is long (Choisnard and Laroche, 2008;Jiang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The "trial" wind vector value that corresponds to a minimum J is the optimum wind vector. Jiang et al (2017) proposed the DNVAR solution of the variational model. They introduced a damped Newton method to form the iteration function of the optimum wind vector.…”

Section: Introduction Of the Variational Model Inversion Model For Wind Retrievalmentioning

confidence: 99%

“…EnVAR, MCVAR, and DNVAR require many iterations to calculate the optimum solution. The wind retrieval accuracy and time latency of the three methods were tested by Jiang et al (2017) on simulated data and Envisat/advanced synthetic aperture radar (ASAR) data. The experimental results show that the retrieval accuracies of the three methods are slightly different, and all of the errors are smaller than the background wind errors.…”

Section: Introduction Of the Variational Model Inversion Model For Wind Retrievalmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

Zhang

Jiang²,

Xiang

et al. 2020

Front. Earth Sci.

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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.

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“…The basic iterative method2,[7][8][9][10] for the inversion of wind speed by dual-frequency altimeter under rainfall condition isFirst, estimate whether it is rain according to the backscatter coefficient statistical model of between o C σ and o Ku σ . If it is not rain, jump out of iteration; If it is rain, ' o Ku σ is substituted into the statistical model to obtain the ideal backscatter coefficient of Ku band ' o Ku σ , and then the signal attenuation of Ku band is obtained by comparing with it,…”

mentioning

confidence: 99%