Impact of Ships and Ocean Fronts on Coastal Sea Surface Wind Measurements From the Advanced Scatterometer

Xu, Qing; Li, Yi‐Zhi; Li, Xiaofeng; Zhang, Zenghai; Cao, Yuenan; Cheng, Yongcun

doi:10.1109/jstars.2018.2817568

Cited by 20 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the difference in NRCS increased significantly at wind speeds greater than 20 m/s. This kind of behavior also exists in the SAR wind retrieval [43] in the presence of ocean thermal fronts. At low sea states (SWH < 3 m), the difference in NRCS was less than 0.3 m, whereas the difference in NRCS increased at greater SWH conditions.…”

mentioning

confidence: 68%

Evaluation of Sea Surface Winds and Waves Retrieved From the Chinese HY-2B Data

Shao¹,

Jiang²,

Jiang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The wind and wave derived from the scatterometer 2 and altimeter on board the Chinese Haiyang-2B (HY-2B) satellite 3 were systemically evaluated in this study. The analysis of 4 matchups between Advanced Scatterometer (ASCAT) and the 5 winds from HY-2B scatterometer with wind speeds up to 25 m/s 6 showed a 0.78 m/s Root-Mean-Square Error (RMSE) with a 0.97 7 Correlation (COR), superior to the 1.2 m/s RMSE and 0.93 COR 8 of winds from the altimeter of HY-2B. Simultaneously, significant 9 wave heights (SWH) measured from HY-2B were collocated with 10 measurements from the Jason-3 altimeter. Analysis of the 11 calibration concluded that the observations from the HY-2B 12 altimeter performed well at low-to-moderate sea states, yielding 13 an RMSE of 0.29 m for SWH with a 0.98 COR for geophysical 14 data records (GDR) data after systematic corrections using the 15 MOE determination method. In particular there were 20 16 typhoons occurring in China Seas during 2019-2020. The 17 evaluation of high SWH when compared with those (SWH > 7 m) 18 from Jason-3/WW3 indicated that HY-2B is accurate, with a 0.87 19 m/0.68 m RMSE and a 0.85 COR with SWH. Finally, statistical 20 analysis of wind speed and SWH was investigated through waves 21 simulated from the WAVEWATCH-III (WW3) / HYbrid 22 Coordinate Ocean Model (HYCOM) using the current and the 23 sea surface temperature (SST). The HY-2B wind measurements 24 performed well at sea surface currents below 1 m/s and SWH less 25 than 3 m, while a term for SST should be included in the 26 scatterometer wind speed retrieval. In addition, wave states likely 27 affect the accuracy of HY-2B wave speed measurements.

show abstract

mentioning

confidence: 68%

Evaluation of Sea Surface Winds and Waves Retrieved From the Chinese HY-2B Data

Shao¹,

Jiang²,

Jiang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…It has generally been found that wind speed has an exponential relationship with NRCS (Masuko et al, 1986), which allowed the development of the geophysical model function (GMF) for SAR wind retrieval (Stoffelen and Anderson, 1997), although a complex sea state characterized these SAR images. In recent research, the performance of C-band model (CMOD) wind retrieval was investigated under such conditions, revealing that ocean fronts cause an underestimation of retrieved wind speed with respect to reference buoy measurements (Xu et al, 2018). The azimuthal cut-off wavelength should have a linear relationship with wind speed in the fully developed sea state (Corcione et al, 2018b).…”

Section: Analysis Resultsmentioning

confidence: 99%

“…In particular, ocean fronts are mainly generated by changes in atmospheric stability due to air-sea temperature differences and sea surface currents (Kim et al, 2014). Under these circumstances, SAR wind retrieval using geophysical model functions is distorted (Xu et al, 2018;Hersbach et al, 2007;Hersbach, 2010). Theoretically, the wave field itself is affected by ocean fronts due to the curl of sea surface currents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of waves observed by synthetic aperture radar across ocean fronts

et al. 2020

View full text Add to dashboard Cite

In this study, synthetic aperture radar (SAR) imaging of waves across ocean fronts was investigated using C-band Sentinel-1 VV-polarized SAR imagery collected over the Yangtze and the Zhujiang estuaries. The presence of ocean fronts in the study area was confirmed by collocated sea surface temperature (SST) data provided by the Advanced Very High Resolution Radiometer (AVHRR) and sea surface current information from the National Ocean Partnership Program (NOPP) based on the HYbrid Coordinate Ocean Model (HYCOM). The experimental results revealed that as the current speed increased, the cut-off wavelength (𝜆 𝑐 ) increased as well. The effect of the increasing azimuth cut-off wavelength, however, was relatively weak in terms of variations of the normalized radar cross-section (NRCS), i.e., it was within 2 dB for 𝜆 𝑐 ≤ 60 m. Hence, it was weaker than the NRCS variation related to SST. Larger NRCS variations (i.e., within 5 dB) occurred for 𝜆 𝑐 values up to 120 m. In addition, the experimental results also demonstrated that the parameterized first-guess spectrum method (PFSM) wave retrieval performance was affected by ocean fronts. In particular, overestimations occurred when ocean fronts were present and 𝜆 𝑐 was <100 m.

show abstract

“…Although this method works well for homogeneous images, other marine phenomena [35], e.g., eddies and internal waves, also produce the same feature on the spatial scale of several kilometers, and these non-wind streaks contaminate the wind direction retrieval. In addition, rain cells [36], up/down-welling, and fronts [37] also distort the homogeneity of the image. The third method is to utilize the linear features in SAR images by using image processing techniques, e.g., local gradient [38] and wavelet analysis [39][40][41][42][43].…”

Section: Machine Learning-based Wind Direction Retrieval From Quad-po...mentioning

confidence: 99%

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Shao,

Zhou,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

In our study, an intelligent method for inverting wind direction from quad-polarized Gaofen-3 (GF-3) synthetic aperture radar (SAR) images is proposed. Specifically, 11300 acquired in wave (WAV) mode are used to retrieve the wind directions using a spectrum-transformation approach and prior information from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis at version 5 (ERA-5) data at 1-hour intervals with 0.25° grids. The dependence of the wind direction on the polarimetric correlation coefficient (PCC) between the co-(vertical-vertical (VV) and horizontal-horizontal (HH)) and cross-polarization (vertical-horizontal (VH) and horizontal-vertical (HV)) channels is studied. It is found that the PCCs in four combination polarizations have asymmetric characteristics with respect to the wind direction with correlation coefficients (CORs) of greater than 0.4 or less than -0.4. Following this rationale, the scheme for inverting wind direction from quad-polarized SAR is trained according to machine learning, in which the matrix PCCs, wind directions, azimuthal angles, and slopes from SAR intensity spectra at the peaks are used as inputs. Subsequently, this intelligent approach is applied to 1300 images in quad-polarization stripmap (QPS) mode, and the retrieval results are validated against advanced scatterometer (ASCAT) measurements. The statistical analysis shows that the root mean squared error (RMSE) of the wind direction is 17.7°, the COR is 0.98, and the scatter index (SI) is 0.11. In addition, the wind speeds inverted using a geophysical model function (GMF) CSARMOD-GF are compared with well-calibrated ASCAT products, resulting in an RMSE of 1.85 m/s, a COR of 0.78, and an SI of 0.28 for the wind speed. Thus, this work provides an automatic scheme for inverting wind from quad-polarized GF-3 SAR images without any external information.

show abstract

Impact of Ships and Ocean Fronts on Coastal Sea Surface Wind Measurements From the Advanced Scatterometer

Cited by 20 publications

References 37 publications

Evaluation of Sea Surface Winds and Waves Retrieved From the Chinese HY-2B Data

Evaluation of Sea Surface Winds and Waves Retrieved From the Chinese HY-2B Data

Analysis of waves observed by synthetic aperture radar across ocean fronts

Machine Learning-Based Wind Direction Retrieval From Quad-Polarized Gaofen-3 SAR Images

Contact Info

Product

Resources

About