Статті в журналах: "Cfosat"

1

Xu, Jingwei, Huanping Wu, Ying Xu, Nikolay V. Koldunov, Xiuzhi Zhang, Lisha Kong, Min Xu, Klaus Fraedrich, and Xiefei Zhi. "Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters." Remote Sensing 15, no. 4 (February 11, 2023): 1005. http://dx.doi.org/10.3390/rs15041005.

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The offshore waters of China are a typical monsoon−affected area where the significant wave height (SWH) is strongly influenced by the different seasonal mean flow in winter and summer. However, limited in situ validations of the SWH have been performed on the China–France Oceanography Satellite (CFOSAT) in these waters. This study focused on validating CFOSAT nadir SWH data with SWH data from in situ buoy observations for China’s offshore waters and the Haiyang−2B (HY−2B) satellite, from July 2019 to December 2021. The validation against the buoy data showed that the relative absolute error has a seasonal cycle, varying in a narrow range near 35%. The RMSE of the CFOSAT nadir SWH was 0.29 m when compared against in situ observations, and CFOSAT was found to be more likely to overestimate the SWH under calm sea conditions. The sea−surface winds play a key role in calm sea conditions. The spatial distributions of the CFOSAT and HY−2B seasonal SWHs were similar, with a two−year mean SWH−field correlation coefficient of 0.98. Moreover, the coherence between the two satellites’ SWH variance increased with SWH magnitude. Our study indicates that, in such typical monsoon−influenced waters, attention should be given to the influence of sea conditions on the accuracy of CFOSAT SWH, particularly in studies that combine data from multiple, long−duration space−based sensors.

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2

Li, Songlin, Huaming Yu, Kejian Wu, Xunqiang Yin, Shuyan Lang, and Jiacheng Ye. "Validation of the Ocean Wave Spectrum from the Remote Sensing Data of the Chinese–French Oceanography Satellite." Remote Sensing 15, no. 16 (August 8, 2023): 3918. http://dx.doi.org/10.3390/rs15163918.

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Since the launch of CFOSAT on 29 October 2018, more than three years of observational data of ocean wave spectra with a frequency range of 0.02–0.26 Hz and a horizontal resolution of 70–90 km have been obtained. This study compares wave spectra retrieved from 6°, 8°, and 10° incidence angle beams and their combination provided by CFOSAT with corresponding data from 98 buoys from the National Data Buoy Center (NDBC) in order to validate the remote sensing wave spectral accuracy from 1 January 2020 to 31 December 2022. The correlation coefficient of frequency spectra (Rs) between CFOSAT and buoys is used to represent the accuracy of the spectral form; the root mean square (RMS) of the significant wave height (SWH) is used to represent the accuracy of the total energy. The results indicate that CFOSAT can retrieve reliable wave frequency spectral forms with a high significant wave height (Rs > 0.8 when SWH > 3 m; RS < 0.4 when SWH < 1 m). The low-frequency noise in the swell part causes the main error, the RMS of the swell height is 0.4 m whereas the RMS of wind wave height is 0.24 m, and the mask filter used for spectral partitioned provided by CFOSAT can eliminate the low-frequency noise and improve the Rs of 10° beam wave spectra from 0.59 to 0.64. For the wind wave spectra, the correct spectra have been achieved and the mask filter cannot improve the accuracy. The wave spectra from the 10° beam without mask filtering provides the best estimation of total energy, the RMS of SWH is 0.23 m, after the mask filtering, the best estimation of spectral form can be achieved, the Rs is 0.64. The novelty of this study is that we found the strong correlation between SWH and Rs, where the scatter of SWH and Rs can be fitted as: Rs = 1 − exp(−0.89·SWH + 0.20); according to this approximate formula, we can estimate the reliability of wave spectra provided by CFOSAT according to the SWH in any region, which is important for wave spectral assimilation in the numerical model. The validation of wave direction indicates that the accuracy of wave spectra in the directional component is poor; further research is needed on the causes of directional errors. Generally, this study is not only an evaluation of the quality of the CFOSAT spectral data, but also an important reference for a series of research requiring the CFOSAT spectral data.

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3

Ye, Haijun, Junmin Li, Bo Li, Junliang Liu, Danling Tang, Wuyang Chen, Hongqiang Yang, et al. "Evaluation of CFOSAT Scatterometer Wind Data in Global Oceans." Remote Sensing 13, no. 10 (May 14, 2021): 1926. http://dx.doi.org/10.3390/rs13101926.

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The China-France Oceanography SATellite (CFOSAT), launched on 29 October 2018, is a joint mission developed by China and France. To evaluate the CFOSAT wind product, L2B swath data with a spatial resolution of 25 × 25 km were compared with in situ measurements between December 2018 and December 2020. The in situ measurements were collected from 217 buoys. All buoy winds were adjusted to 10 m height using a simple logarithmic correction method. The temporal and spatial separations between the CFOSAT and in situ measurements were restricted to less than 30 min and 0.25°. The results indicate that the CFOSAT wind retrievals agree well with the buoy measurements. The root mean square errors (RMSEs) of wind vectors were 1.39 m s−1 and 34.32° and negligible biases were found. In the near shore under rain-free conditions, the RMSEs were enhanced to 1.42 m s−1 and 33.43°. Similarly, the RMSEs were reduced to 1.16 m s−1 and 30.41° offshore after the rain effect was removed. After winds less than 4 m s−1 were removed, the RMSE of wind directions was reduced to 19.69°. The effects of significant wave height, air-sea temperature difference, sea surface temperature, atmospheric pressure and ocean surface current on the wind residuals were assessed. The performance of wind retrievals under the passage of tropical cyclones was evaluated. The evaluation results show that the CFOSAT wind retrievals satisfy the accuracy requirements of scientific research, although some improvements are needed to enhance the performance.

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4

DONG Xiaolong, ZHU Di, LIN Wenming, ZHANG Kuo, YUN Risheng, LIU Jianqiang, LANG Shuyan, DING Zhenyu, MA Jianying, and XU Xingou. "Orbit Performances Validation for CFOSAT Scatterometer." Chinese Journal of Space Science 40, no. 3 (2020): 425. http://dx.doi.org/10.11728/cjss2020.03.425.

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5

Li, Zhen, Anton Verhoef, and Ad Stoffelen. "Bayesian Sea Ice Detection Algorithm for CFOSAT." Remote Sensing 14, no. 15 (July 25, 2022): 3569. http://dx.doi.org/10.3390/rs14153569.

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This paper describes the adaptation of the Bayesian sea ice detection algorithm for the rotating fan-beam scatterometer CSCAT onboard the China–France Oceanography Satellite (CFOSAT). The algorithm was originally developed and applied for fixed fan-beam and rotating pencil-beam scatterometers. It is based on the probability of the wind and ice backscatter distances from the measurements to their corresponding geophysical model functions (GMFs). The new rotating Ku-band fan-beam design introduces very diverse geometry distributions across the swath, which leads to three main adaptations of the algorithm: (1) a new probability distribution function fit for the backscatter distances over open sea; (2) a linear ice GMF as a function of incidence angle; (3) the separation of outer swath wind vector cells ((WVCs) number 1, 2, 41, 42) from the other WVCs to form two sets of probability distribution function fits for these two WVC groups. The results are validated against sea ice extents from the active microwave ASCAT and the passive microwave SSMI. The validation shows good agreement with both instruments, despite the discrepancies with SSMI during the melting season, and this discrepancy is caused by the lower sensitivity of the passive microwave to detect the ice at a low concentration with a mixed water/ice state, while the scatterometer is more tolerant regarding this situation. We observed that the sea-ice GMF regression between HH and VV sea-ice backscatter at low and high incidence angles decorrelates at around −12 dB (28) and −20 dB (50) and an experiment with truncated backscatter values at these incidence angles is executed, which significantly improves the year-long average sea ice extents. In conclusion, the adapted algorithm for CSCAT works effectively and yields consistent sea ice extents compared with active and passive microwave instruments. As such, it can, in principle, contribute to the long-term global scatterometer sea ice record, and as the algorithm was adapted for a rotating fan-beam scatterometer, it also can serve as a guideline for the recently launched, dual-frequency, rotating fan-beam scatterometer WindRAD.

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6

Zhao, Xiaokang, Wenming Lin, Marcos Portabella, Zhixiong Wang, and Yijun He. "Effects of rain on CFOSAT scatterometer measurements." Remote Sensing of Environment 274 (June 2022): 113015. http://dx.doi.org/10.1016/j.rse.2022.113015.

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7

Li, Bo, Junmin Li, Shilin Tang, Ping Shi, Wuyang Chen, and Junliang Liu. "Evaluation of CFOSAT Wave Height Data with In Situ Observations in the South China Sea." Remote Sensing 15, no. 4 (February 6, 2023): 898. http://dx.doi.org/10.3390/rs15040898.

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The wave spectrometer operated by the China–France Oceanography Satellite (CFOSAT) can provide global ocean wave observation data. Although a lot of work on calibration and verification has been carried out in the open oceans dominated by swells, the quality of the data in the relatively enclosed sea area with complex terrain still lacks sufficient examination. The objective of this study is to assess the performance of the significant wave height data of the CFOSAT in the South China Sea (SCS), a unique sea area characterized by semi-enclosed basin and multi-reef terrain, and to recognize the environmental factors affecting the data quality. Compared against the long-term observations from five mooring or buoy sites, we find that the data is well performed in the relatively open and deep areas of the SCS, with an average correlation coefficient as high as 0.87, and a low average root-mean-square error of 0.47 m. However, the combined effects of complex topography, monsoons, and swell proportion variation will affect the performance of data. In the southern deep areas, the waves may be affected by a large number of dotted reefs, leading to wave deformations and energy dissipation in different seasons. In the northern nearshore areas, waves tend to be sheltered by the land or distorted by the shallow topography effects. These processes make it difficult for the swell to fully develop as in the open oceans. The low proportion of swell is a disadvantage for the CFOSAT to correctly observe the wave data and may lead to possible errors. Our results emphasize the importance of more verification when applying the CFOSAT data in certain local seas, and the necessity to adjust the algorithm of inverting wave spectra according to specific environmental factors.

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8

Li, Zheng, Bingcheng Wan, Zexia Duan, Yuanhong He, Yingxin Yu, and Huansang Chen. "Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations." Remote Sensing 15, no. 17 (August 25, 2023): 4172. http://dx.doi.org/10.3390/rs15174172.

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This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area.

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9

Li, Bo, Junmin Li, Junliang Liu, Shilin Tang, Wuyang Chen, Ping Shi, and Yupeng Liu. "Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks." Remote Sensing 14, no. 3 (February 7, 2022): 773. http://dx.doi.org/10.3390/rs14030773.

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The wave data measured by CFOSAT (China France Oceanography Satellite) have been validated mainly based on numerical model outputs and altimetry products on a global scale. It is still necessary to further calibrate the data for specific regions, e.g., the southern South China Sea. This study analyses the practicability of calibrating the dominant wavelength by using artificial neural networks and mean impact value analysis based on two sets of buoy data with a 2-year observation period and contemporaneous ERA5 reanalysis data. The artificial neural network modeling experiments are repeated 1000 times randomly by Monte Carlo methods to avoid sampling uncertainty. Both experimental results based on the random sampling method and chronological sampling method are performed. Independent buoy observations are used to validate the calibration model. The results show that although there are obvious differences between the CFOSAT wavelength data and the field observations, the parameters observed by the satellite itself can effectively calibrate the data. In addition to the wavelength, nadir significant wave height, nadir wind speed, and the distance between the calibration point and satellite observation point are the most important parameters for the calibration. Accurate data from other sources, such as ERA5, would be helpful to further improve the calibration results. The variable contributing the most to the calibration effect is the mean wave period, which virtually provides relatively accurate wavelength information for the calibration network. These results verify the possibility of synchronous self-calibration for the CFOSAT wavelength data and provide a reference for the further calibration of the satellite products in other regions.

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10

Shao, Weizeng, Tao Jiang, Yu Zhang, Jian Shi, and Weili Wang. "Cyclonic Wave Simulations Based on WAVEWATCH-III Using a Sea Surface Drag Coefficient Derived from CFOSAT SWIM Data." Atmosphere 12, no. 12 (December 2, 2021): 1610. http://dx.doi.org/10.3390/atmos12121610.

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It is well known that numerical models are powerful methods for wave simulation of typhoons, where the sea surface drag coefficient is sensitive to strong winds. With the development of remote sensing techniques, typhoon data (i.e., wind and waves) have been captured by optical and microwave satellites such as the Chinese-French Oceanography SATellite (CFOSAT). In particular, wind and wave spectra data can be simultaneously measured by the Surface Wave Investigation and Monitoring (SWIM) onboard CFOSAT. In this study, existing parameterizations for the drag coefficient are implemented for typhoon wave simulations using the WAVEWATCH-III (WW3) model. In particular, a parameterization of the drag coefficient derived from sea surface roughness is adopted by considering the terms for wave steepness and wave age from the measurements from SWIM products of CFOSAT from 20 typhoons during 2019–2020 at winds up to 30 m/s. The simulated significant wave height (Hs) from the WW3 model was validated against the observations from several moored buoys active during three typhoons, i.e., Typhoon Fung-wong (2014), Chan-hom (2015), and Lekima (2019). The analysis results indicated that the proposed parameterization of the drag coefficient significantly improved the accuracy of typhoon wave estimation (a 0.49 m root mean square error (RMSE) of Hs and a 0.35 scatter index (SI)), greater than the 0.55 RMSE of Hs and >0.4 SI using other existing parameterizations. In this sense, the adopted parameterization for the drag coefficient is recommended for typhoon wave simulations using the WW3 model, especially for sea states with Hs < 7 m. Moreover, the accuracy of simulated waves was not reduced with growing winds and sea states using the proposed parameterization. However, the applicability of the proposed parameterization in hurricanes necessitates further investigation at high winds (>30 m/s).

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11

Mou, Xiaoheng, Wenming Lin, and Yijun He. "Towards a Consistent Wind Data Record for the CFOSAT Scatterometer." Remote Sensing 15, no. 8 (April 14, 2023): 2081. http://dx.doi.org/10.3390/rs15082081.

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Since launch, the Ku-band rotating fan-beam scatterometer onboard the China–France Oceanography Satellite (CFOSAT) has provided valuable sea surface wind measurements for more than four years. The performance of CFOSAT scatterometer (CSCAT)-derived wind vectors is generally good in terms of root-mean-square error, while the absolute calibration error remains an issue in the current CSCAT product. In this paper, the temporal variation in CSCAT winds is overviewed by analyzing the collocated CSCAT and numerical weather prediction (NWP) model winds. Then, the reasons for the inconsistency of CSCAT-retrieved winds are discussed. The results show that the imperfect calibration of radar backscatter coefficients is likely the main problem of CSCAT wind processing. Consequently, a running-window-based (i.e., weekly) ocean calibration is proposed to evaluate the consistency of CSCAT radar backscatters, and in turn, to recalibrate CSCAT backscattering measurements before the reprocessing of CSCAT wind data. Although the proposed method is not feasible for the near-real-time processing of CSCAT data, it significantly mitigates the temporal variations in CSCAT wind speed bias, resulting in a more consistent CSCAT wind data record that may be beneficial to meteorological quantitative applications.

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12

Jiang, Haoyu, Yuhao Song, Alexey Mironov, Zheng Yang, Ying Xu, and Jianqiang Liu. "Accurate mean wave period from SWIM instrument on-board CFOSAT." Remote Sensing of Environment 280 (October 2022): 113149. http://dx.doi.org/10.1016/j.rse.2022.113149.

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13

Zhang, Kuo, Xiaolong Dong, Di Zhu, Risheng Yun, Binghua Wang, and Miaomiao Yu. "An Improved Method of Noise Subtraction for the CFOSAT Scatterometer." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 7506–15. http://dx.doi.org/10.1109/jstars.2021.3093559.

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14

Liu, Jianqiang, Wenming Lin, Xiaolong Dong, Shuyan Lang, Risheng Yun, Di Zhu, Kuo Zhang, et al. "First Results From the Rotating Fan Beam Scatterometer Onboard CFOSAT." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12 (December 2020): 8793–806. http://dx.doi.org/10.1109/tgrs.2020.2990708.

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15

ZHAO, Xiaokang, Wenming LIN, Zhixiong WANG, and Yijun HE. "Effects of Sea Surface Temperature on the CFOSAT Scatterometer Measurements." Chinese Journal of Space Science 42 (2023): 1. http://dx.doi.org/10.11728/cjss2023.01.220216017.

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16

Liang, Guozhou, Jungang Yang, and Jichao Wang. "Accuracy Evaluation of CFOSAT SWIM L2 Products Based on NDBC Buoy and Jason-3 Altimeter Data." Remote Sensing 13, no. 5 (February 26, 2021): 887. http://dx.doi.org/10.3390/rs13050887.

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Chinese-French Oceanography Satellite (CFOSAT), the first satellite which can observe global ocean wave and wind synchronously, was successfully launched On 29 October 2018. The CFOSAT carries SWIM that can observe ocean wave on a global scale. Based on National Data Buoy Center (NDBC) buoys and Jason-3 altimeter data, this study evaluated the accuracy of L2 level products of CFOSAT SWIM from August 2019 to September 2020. The results show that the accuracy of the nadir Significant Wave Height (SWH) data of the SWIM wave spectrometer is good. Compared with the data of the NDBC buoys and Jason-3 altimeter, the RMSE of the nadir box SWH were 0.39 and 0.21 m, respectively. The variation trend of SWH were first increasing and then decreasing with the increasing of the wave height. The precision of off-nadir wave spectrum SWH is not better than nadir box SWH data. Accuracy was evaluated for off-nadir data from August 2019 to June 2020 and after June 2020, respectively. After linear regression correction, the accuracy of off-nadir wave spectrum SWH was improved. The data accuracy evaluation and comparison of different time period showed that the off-nadir wave spectrum SWH accuracy was improved after the data version was updated in June 2020, especially for 6° and 8° wave spectrum. The precision of off-nadir wave spectrum SWH decreases with the increasing of wave height. The accuracy of the dominant wave direction of each wave spectrum is also not very good, and the accuracy of the dominant wave direction of 10° wave spectrum is slightly better than the others. In general, the accuracy of SWIM nadir beam SWH data reaches the high data accuracy of traditional altimeter, while the accuracy of off-nadir wave spectrum SWH is less than that of nadir beam SWH data. The off-nadir SWH data accuracy after June 2020 has been greatly improved.

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17

Zhai, Xiaochun, Zhixiong Wang, Zhaojun Zheng, Rui Xu, Fangli Dou, Na Xu, and Xingying Zhang. "Sea Ice Monitoring with CFOSAT Scatterometer Measurements Using Random Forest Classifier." Remote Sensing 13, no. 22 (November 19, 2021): 4686. http://dx.doi.org/10.3390/rs13224686.

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The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad).

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18

Hauser, Danièle. "Observations du vent de surface et des vagues à partir de CFOSAT." La Météorologie, no. 104 (2019): 2. http://dx.doi.org/10.4267/2042/69775.

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19

Lin, Wenming, Xiaolong Dong, Marcos Portabella, Shuyan Lang, Yijun He, Risheng Yun, Zhixiong Wang, Xingou Xu, Di Zhu, and Jianqiang Liu. "A Perspective on the Performance of the CFOSAT Rotating Fan-Beam Scatterometer." IEEE Transactions on Geoscience and Remote Sensing 57, no. 2 (February 2019): 627–39. http://dx.doi.org/10.1109/tgrs.2018.2858852.

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20

Yurovskaya, Maria, Vladimir Kudryavtsev, Alexey Mironov, Alexis Mouche, Fabrice Collard, and Bertrand Chapron. "Surface Wave Developments under Tropical Cyclone Goni (2020): Multi-Satellite Observations and Parametric Model Comparisons." Remote Sensing 14, no. 9 (April 23, 2022): 2032. http://dx.doi.org/10.3390/rs14092032.

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Over the Philippine Sea, the tropical cyclone (TC) Goni reaches category 5 on 29–31 October 2020. Multi-satellite observations, including CFOSAT SWIM/SCAT and Sentinel-1 SAR data, are jointly analyzed to assess the performances of a parametric model. Recently developed to provide a fast estimation of surface wave developments under rapidly evolving TCs, this full 2D parametric model (KYCM) and its simplified self-similar solutions (TC-wave geophysical model function (TCW GMF)) are thoroughly compared with satellite observations. TCW GMF provides immediate first-guess estimates, at any location in space and time, for the significant wave height, wavelength, and wave direction parameters. Moving cyclones trigger strong asymmetrical wave fields, associated to a resonance between wave group velocity and TC heading velocity. For TC Goni, this effect is well evidenced and captured, leading to extreme waves reaching up to 8 m, further outrunning as swell systems with wavelengths about 200–250 m in the TC heading direction, slightly shifted leftwards. Considering wind field constrained with very highly resolved Sentinel-1 SAR measurements and medium resolution CFOSAT SCAT data, quantitative agreements between satellite measurements and KYCM/TCW GMF results are obtained. Far from the TC inner core (∼10 radii of maximum wind speed), the superposition of outrunning swell systems and local wind waves estimates leads to Hs values very close to altimeter measurements. This case study demonstrates the promising capabilities to combine multi-satellite observations, with analytical self-similar solutions to advance improved understandings of surface wave generation under extreme wind conditions.

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21

WANG, Yihui, Xingou XU, and Ying XU. "Comparisons on One-dimensional Ocean Wave Spectrum Models Based on SWIM/CFOSAT Observations." Chinese Journal of Space Science 43, no. 6 (2023): 1. http://dx.doi.org/10.11728/cjss2023.06.2023-0068.

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22

Quan, Meixuan, Jie Zhang, and Rui Zhang. "A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer." Remote Sensing 16, no. 5 (March 2, 2024): 887. http://dx.doi.org/10.3390/rs16050887.

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The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products.

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23

Liu, Meijie, Ran Yan, Jie Zhang, Ying Xu, Ping Chen, Lijian Shi, Jin Wang, Shilei Zhong, and Xi Zhang. "Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles." Remote Sensing 14, no. 1 (December 25, 2021): 91. http://dx.doi.org/10.3390/rs14010091.

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Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

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Sun, Meng, Jianting Du, Yongzeng Yang, and Xunqiang Yin. "Evaluation of Assimilation in the MASNUM Wave Model Based on Jason-3 and CFOSAT." Remote Sensing 13, no. 19 (September 25, 2021): 3833. http://dx.doi.org/10.3390/rs13193833.

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Accurate numerical simulation of ocean waves is one of the most important measures to ensure shipping safety, offshore engineering construction, etc. The use of wave observations from satellite is an efficient way to correct model results. The goal of this paper is to assess the performance of assimilation in the MASNUM wave model for the Indian Ocean. The assimilation technique is based on Ensemble Adjusted Kalman Filter, with a variable ensemble constructed by the dynamic sampling method rather than ensemble members of wave model. Observations of significant wave height from satellites Jason-3 and CFOSAT are regarded as assimilation data and independent validation data, respectively. The results indicate good performance in terms of absolute mean error for significant wave height. Model error decreases by roughly 20–40% in high-sea conditions.

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25

Zhang, Kuo, Xiaolong Dong, Di Zhu, and Risheng Yun. "Estimation and Correction of Geolocation Errors of the CFOSAT Scatterometer Using Coastline Backscatter Coefficients." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 53–61. http://dx.doi.org/10.1109/jstars.2020.3035238.

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26

Zhu, Jintai, Xiaolong Dong, Wenming Lin, and Di Zhu. "A Preliminary Study of the Calibration for the Rotating Fan-Beam Scatterometer on CFOSAT." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 2 (February 2015): 460–70. http://dx.doi.org/10.1109/jstars.2014.2333241.

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27

Xu, Ying, Jianqiang Liu, Lingling Xie, Congrong Sun, Jinpu Liu, Junyi Li, and Di Xian. "China-France Oceanography Satellite (CFOSAT) simultaneously observes the typhoon-induced wind and wave fields." Acta Oceanologica Sinica 38, no. 11 (November 2019): 158–61. http://dx.doi.org/10.1007/s13131-019-1506-3.

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28

DONG, Ying, and Wenming LIN. "Analysis of Sea Surface Backscatter Coefficient Errors and Its Effects for the CFOSAT Scatterometer." Chinese Journal of Space Science 44, no. 2 (2024): 1. http://dx.doi.org/10.11728/cjss2024.02.2023-0144.

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29

Zhai, Xiaochun, Rui Xu, Zhixiong Wang, Zhaojun Zheng, Yixuan Shou, Shengrong Tian, Lin Tian, Xiuqing Hu, Lin Chen, and Na Xu. "Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier." Remote Sensing 15, no. 5 (February 27, 2023): 1310. http://dx.doi.org/10.3390/rs15051310.

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The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

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30

SHIMURA, Tomoya, Yukino NISHIMURA, Nobuhito MORI, Takuya MIYASHITA, Yasuyuki BABA, Sho SHIMAMURA, and Tomohiro YASUDA. "MULTI PERSPECTIVE OBSERVATION OF TYPHOON WAVES IN 2021 - COMPARISON AMONG GPS WAVE BUOYS, CFOSAT, AND MODEL -." Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering) 78, no. 2 (2022): I_397—I_402. http://dx.doi.org/10.2208/kaigan.78.2_i_397.

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31

Xu, Ming, and Li Huang. "An analytic algorithm for global coverage of the revisiting orbit and its application to the CFOSAT satellite." Astrophysics and Space Science 352, no. 2 (April 30, 2014): 497–502. http://dx.doi.org/10.1007/s10509-014-1939-2.

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32

Karaev, V. Yu, M. A. Panfilova, M. S. Ryabkova, Yu A. Titchenko, E. M. Meshkov, and X. Li. "Retrieval of the two-dimensional slope field by the SWIM spectrometer of the CFOSAT satellite: discussion of the algorithm." Russian Journal of Earth Sciences 21, no. 6 (December 23, 2021): 1–9. http://dx.doi.org/10.2205/2021es000784.

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33

HUANG, Bingqing, Xiaoming LI, and Qiongqiong CAI. "Retrieval of ocean wave spectra from Sentinel-1 SAR data and comparison with the CFOSAT/SWIM data in the Arctic ocean." National Remote Sensing Bulletin 27, no. 4 (2023): 881–90. http://dx.doi.org/10.11834/jrs.20211210.

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34

Wang, Jin, Brandon J. Bethel, Changming Dong, Chunhui Li, and Yuhan Cao. "Numerical Simulation and Observational Data Analysis of Mesoscale Eddy Effects on Surface Waves in the South China Sea." Remote Sensing 14, no. 6 (March 18, 2022): 1463. http://dx.doi.org/10.3390/rs14061463.

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Surface current velocities of mesoscale eddies have a unique annular structure, which can inevitably influence surface wave properties and energy distribution. Sensitivity experiments of ideal mesoscale eddies on waves were carried out by the Simulating WAves Nearshore (SWAN) wave model to investigate these influences. In addition, China–France Oceanography SATellite Surface Wave Investigation and Monitoring (CFOSAT-SWIM) observational data of a large warm-cored eddy in the South China Sea (SCS) during the period of October–November 2019 were used to validate the influence of mesoscale eddies on waves. The results illustrated that mesoscale eddies can alter wave properties (wave height, period, and steepness) by 20–30%. Moreover, wave direction could also be modified by 30°–40°. The current effect on waves (CEW) was more noticeable with strong currents and weak winds, and was governed by wave age and the ratio of wave group velocity to current velocity. Wave spectra clearly indicated that current-induced variability in wave energy distribution happens on a spatial scale of 5–90 km (i.e., the sub- and mesoscales). Through comparing the difference of wave energy on both sides of an eddy perpendicular to the wave propagation direction in an eddy, a simple way to trace the footprints of waves on eddies was devised.

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35

Gibert, Ferran, Jacqueline Boutin, Wolfgang Dierking, Alba Granados, Yan Li, Eduard Makhoul, Junmin Meng, et al. "Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications." Remote Sensing 13, no. 14 (July 20, 2021): 2847. http://dx.doi.org/10.3390/rs13142847.

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This paper provides an overview of the Dragon 4 project dealing with operational monitoring of sea ice and sea surface salinity (SSS) and new product developments for altimetry data. To improve sea ice thickness retrieval, a new method was developed to match the Cryosat-2 radar waveform. Additionally, an automated sea ice drift detection scheme was developed and tested on Sentinel-1 data, and the sea ice drifty capability of Gaofen-4 geostationary optical data was evaluated. A second topic included implementation and validation of a prototype of a Fully-Focussed SAR processor adapted for Sentinel-3 and Sentinel-6 altimeters and evaluation of its performance with Sentinel-3 data over the Yellow Sea; the assessment of sea surface height (SSH), significant wave height (SWH), and wind speed measurements using different altimeters and CFOSAT SWIM; and the fusion of SSH measurements in mapping sea level anomaly (SLA) data to detect mesoscale eddies. Thirdly, the investigations on the retrieval of SSS include simulations to analyse the performances of the Chinese payload configurations of the Interferometric Microwave Radiometer and the Microwave Imager Combined Active and Passive, SSS retrieval under rain conditions, and the combination of active and passive microwave to study extreme winds.

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36

Xue, Sihan, Lingsheng Meng, Xupu Geng, Haiyang Sun, Deanna Edwing, and Xiao-Hai Yan. "Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks." Atmosphere 14, no. 8 (August 11, 2023): 1272. http://dx.doi.org/10.3390/atmos14081272.

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Sea surface winds and waves are very important phenomena that exist in the air–sea boundary layer. With the advent of climate change, cascade effects are bringing more attention to these phenomena as warmer sea surface temperatures bring about stronger winds, thereby altering global wave conditions. Synthetic aperture radar (SAR) is a powerful sensor for high-resolution surface wind and wave observations and has accumulated large quantities of data. Furthermore, deep learning methods have been increasingly utilized in geoscience, especially the inversion of ocean information from SAR imagery. Here, we propose a method to invert various parameters of ocean surface winds and waves using Sentinel-1 SAR IW mode data. To ensure this method is more robust and scalable, we augmented the input data with dual-polarized SAR imagery, an incident angle, and a more constrained homogeneity test. This method adopts a deeper structure in order to retrieve more wind and wave parameters, and the use of residual networks can accelerate training convergence and improve regression accuracy. Using 1600 training samples filtered by a novel homogeneity test and with significant wave heights between 0 and 10 m, results from error parameters including the root mean square error (RMSE), scatter index (SI), and correlation coefficient (COR) show the great performance of this proposed method. The RMSE is 0.45 m, 0.76 s, and 1.90 m/s for the significant wave height, mean wave period, and wind speed, respectively. Furthermore, the temporal variation and spatial distribution of the estimates are consistent with China–France Oceanography Satellite (CFOSAT) observations, buoy measurements, WaveWatch3 regional model data, and ERA5 reanalysis data.

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37

Xu, Changjing, Zhixiong Wang, Xiaochun Zhai, Wenming Lin, and Yijun He. "SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements." Remote Sensing 15, no. 6 (March 17, 2023): 1630. http://dx.doi.org/10.3390/rs15061630.

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This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring.

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38

McInnes, Kathleen L., John L. McBride, and Lance M. Leslie. "Cold Fronts over Southeastern Australia: Their Representation in an Operational Numerical Weather Prediction Model." Weather and Forecasting 9, no. 3 (September 1994): 384–409. http://dx.doi.org/10.1175/1520-0434(1994)009<0384:cfosat>2.0.co;2.

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39

Xu, Xingou, Xiaolong Dong, and Yu Xie. "On-Board Wind Scatterometry." Remote Sensing 12, no. 7 (April 9, 2020): 1216. http://dx.doi.org/10.3390/rs12071216.

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Real-time (RT) ocean surface wind can make key improvements to disaster alarmingand safety of maritime navigation to avoid loss in property and human lives. Wind scatterometry is a well-acquainted way of obtaining good quality ocean surface winds, and it has been in application for decades. Existing wind-obtaining chains employ ground stations for receiving observations and can, at best, provide products in around 30 minutes for limited regions. In recent years, a satellite information-obtaining and transmission network is the new trend of Earth observation. In this research, on-board wind retrieval environment and procedures, which are different from traditional wind-obtaining chains, are proposed. First, the establishment of the on-board environment is instructed. Structures of each module are provided. The ground simulation system is been established based on this. After that, existing observing and processing routines of wind scatterometry are described, and then an on-board processing chain proposed and described. Modifications to existing satellite-ground chains are highlighted. The proposed method is validated in Level 0 data from the Chinese–French Oceanic SATellite (CFOSAT). Experiments indicate that the proposed on-board processing procedure can provide comparable results to ground-processed wind products. The root-mean-square error (RMSE) of wind speed for a track of data used in the experiment was about 0.26 m/s, and it was about 0.8° for wind direction. By decreasing wind field result quality, calculation time can be lessened in the on-board environment. However, it is found that in the whole chain of on-board wind generation, the most time-consuming procedure is observation-obtaining. The proposed on-board processing method can achieve good wind accuracy while meeting RT applications with good processing time. This provides a good complement to existing on-board-observing-ground-processing chains for RT applications.

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40

Li, Zhen, Ad Stoffelen, and Anton Verhoef. "A generalized simulation capability for rotating- beam scatterometers." Atmospheric Measurement Techniques 12, no. 7 (July 4, 2019): 3573–94. http://dx.doi.org/10.5194/amt-12-3573-2019.

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Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

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41

Zhang, Yunfeng, Yiming Wang, Yonggang Ji, and Ming Li. "Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation." Remote Sensing 15, no. 11 (June 5, 2023): 2952. http://dx.doi.org/10.3390/rs15112952.

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Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT).

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42

Yao, Ru, Weizeng Shao, Youguang Zhang, Meng Wei, Song Hu, and Juncheng Zuo. "Feasibility of Wave Simulation in Typhoon Using WAVEWATCH-III Forced by Remote-Sensed Wind." Journal of Marine Science and Engineering 11, no. 10 (October 19, 2023): 2010. http://dx.doi.org/10.3390/jmse11102010.

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The purpose of our work was to assess the feasibility of hindcasting waves using WAVEWATCH-III (WW3) in a typhoon by assembling winds from multiple remote-sensed products. During the typhoon season in 2021–2022, the swath wind products in the Western Pacific Ocean were collected from scatterometers and radiometers. Cyclonic winds with a spatial resolution of 0.125° at intervals of 6 h were obtained by assembling the remote-sensed winds from those satellites. The maximum wind speeds, Vmax, were verified using the reanalysis data from the National Hurricane Center (NHC), yielding a root-mean-squared error (RMSE) of 4.79 m/s and a scatter index (SI) value of 0.2. The simulated wave spectrum was compared with the measurements from Surface Waves Investigation and Monitoring (SWIM) carried out on the Chinese–French Oceanography Satellite (CFOSAT), yielding a correlation coefficient (Cor) of 0.80, squared error (Err) of 0.49, RMSE of significant wave height (SWH) of 0.48 m with an SI of 0.25, and an RMSE of the peak wave period (PWP) of 0.95 s with an SI of 0.10. The bias of wave (WW3 minus European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA-5)) concerning the bias of wind (assembling minus ERA-5) showed that the WW3-simulated SWH with the assembling wind forcing was significantly higher than that with the ERA-5 wind forcing. Moreover, the bias of SWH gradually increased with an increasing bias of wind speed; i.e., the bias of SWH increased up to 4 m as the bias of wind speed reached 30 m/s. It was concluded that the assembling wind from multiple scatterometers and radiometers is a promising source for wave simulations via WW3 in typhoons.

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43

Chen, Hua-Pin, I.-Chyn Wey, Liang-Yen Chen, Cheng-Yueh Wu, and San-Fu Wang. "Design and Verification of a New Universal Active Filter Based on the Current Feedback Operational Amplifier and Commercial AD844 Integrated Circuit." Sensors 23, no. 19 (October 5, 2023): 8258. http://dx.doi.org/10.3390/s23198258.

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This paper presents a triple-input and four-output type voltage-mode universal active filter based on three current-feedback operational amplifiers (CFOAs). The filter employs three CFOAs, two grounded capacitors, and six resistors. The filter structure has three high-input and three low-output impedances that simultaneously provide band-reject, high-pass, low-pass, and band-pass filtering functions with single-input and four-output type and also implements an all-pass filtering function by connecting three input signals to one input without the use of voltage inverters or switches. The same circuit configuration enables two unique filtering functions: low-pass notch and high-pass notch. Three CFOAs with three high-input and low-output impedance terminals enable cascading without voltage buffers. The circuit is implemented using three commercial off-the-shelf AD844 integrated circuits, two grounded capacitors, and six resistors and further implemented as a CFOA-based chip using three CFOAs, two grounded capacitors, and six resistors. The CFOA-based chip has lower power consumption and higher integration than the AD844-based filter. The circuit was simulated using OrCAD PSpice to verify the AD844-based filter and Synopsys HSpice for post-layout simulation of the CFOA-based chip. The theoretical analysis is validated and confirmed by measurements on an AD844-based filter and a CFOA-based chip.

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44

Gupta, S. S., D. R. Bhaskar, and R. Senani. "Synthesis of New Single CFOA-Based VCOs Incorporating the Voltage Summing Property of Analog Multipliers." ISRN Electronics 2012 (September 18, 2012): 1–8. http://dx.doi.org/10.5402/2012/463680.

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Recently, current feedback operational amplifier (CFOA) and analog multiplier-(AM-) based-voltage controlled oscillators (VCOs) have been published in the literature which require 2 CFOAs and 2 AMs for linear tuning law between control voltage and frequency of oscillation. In this paper, a family of eight new voltage-controlled oscillators (VCOs), with linear tuning laws, employing only a single CFOA in conjunction with two analog multipliers (AMs), has been derived through a systematic state-variable methodology. This has been made possible by exploiting the voltage summing property of the multiplier chosen which has never been done in the literature earlier. The workability of the presented VCOs has been verified by experimental results based on AD844 type CFOAs and AD534 type AMs, and the advantages of new circuits over the previously known CFOA-AM-based VCOs have been highlighted.

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45

Abuelma’atti, Muhammad Taher, and Husain Abdullah Al-Zaher. "New Grounded-Capacitor Sinusoidal Oscillators Using the Current-Feedback-Amplifier Pole." Active and Passive Electronic Components 21, no. 1 (1998): 23–32. http://dx.doi.org/10.1155/1998/37035.

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Анотація:

New current-feedback-operational amplifier (CFOA)-pole-based sinusoidal oscillator circuits are presented. Each circuit uses two CFOAs, two (or three) grounded capacitors and/or resistors. Experimental results are included.

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46

Han, Lei, Qiyan Ji, Xiaoyan Jia, Yu Liu, Guoqing Han, and Xiayan Lin. "Significant Wave Height Prediction in the South China Sea Based on the ConvLSTM Algorithm." Journal of Marine Science and Engineering 10, no. 11 (November 7, 2022): 1683. http://dx.doi.org/10.3390/jmse10111683.

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Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction.

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47

KILIÇ, RECAI. "EXPERIMENTAL STUDY OF CFOA-BASED INDUCTORLESS CHUA'S CIRCUIT." International Journal of Bifurcation and Chaos 14, no. 04 (April 2004): 1369–74. http://dx.doi.org/10.1142/s021812740400996x.

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The current feedback op amps (CFOAs), with some significant advantages over the conventional op amps, have been used instead of voltage op amps (VOAs) in new implementations of Chua's circuit. In our previous study, after providing a comparative investigation of CFOA-based realizations of Chua's circuit in the literature, we have also presented an alternative inductorless CFOA-based realization of Chua's circuit, and the circuit's chaotic behavior by Pspice simulations. In this paper, we investigate CFOA-based Chua's circuit by constructing an experimental setup, and testing the performance of the proposed implementation at different frequencies. Its excellent high frequency performance was experimentally verified.

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48

GUPTA, S. S., D. R. BHASKAR, R. SENANI, and A. K. SINGH. "SYNTHESIS OF LINEAR VCOs: THE STATE-VARIABLE APPROACH." Journal of Circuits, Systems and Computers 20, no. 04 (June 2011): 587–606. http://dx.doi.org/10.1142/s0218126611007487.

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The paper presents a systematic approach of synthesizing sinusoidal linear VCOs (i.e., an oscillator providing linear tuning law of the type f0 ∝ VC between the oscillation frequency f0 and an external control voltage VC). In view of recent interest on current feedback operational amplifier (CFOA)-based oscillators, the proposed methodology has been demonstrated by deriving a class of linear VCOs employing CFOAs and analog multipliers (AM). The workability of the new VCOs has been established by experimental results of the circuits implemented with commercially available AD844 type CFOAs and AD534 type AMs.

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49

SAMIOTIS, P., and C. PSYCHALINOS. "DESIGN OF FILTERS WITH ONLY GROUNDED PASSIVE ELEMENTS USING DIFFERENTIAL VOLTAGE CURRENT FEEDBACK OPERATIONAL AMPLIFIERS." Journal of Circuits, Systems and Computers 19, no. 03 (May 2010): 573–80. http://dx.doi.org/10.1142/s021812661000630x.

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Differential Voltage Current Feedback Operational Amplifiers (DVCFOAs) and conventional CFOAs are employed as active elements for designing leapfrog filters in this work. DVCFOAs are realized by enhancing the topology of the conventional CFOA in order to have available a second high-impedance input terminal and, simultaneously, establishing a voltage at low-impedance input terminal equal to the difference between the voltages at high-impedance input terminals. A third-order leapfrog filter is realized by employing CFOAs or DVCFOAs, and the most important performance factors of the corresponding configurations have been considered in order to investigate the benefits offered by each topology.

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50

Hao, Mengyu, Weizeng Shao, Shaohua Shi, Xing Liu, Yuyi Hu, and Juncheng Zuo. "Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image." Remote Sensing 15, no. 18 (September 7, 2023): 4402. http://dx.doi.org/10.3390/rs15184402.

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The Chinese-French Oceanography SATellite (CFOSAT) jointly developed by the Chinese National Space Agency (CNSA) and the Centre National d’Etudes Spatiales (CNES) of France carries a wave spectrometer (Surface Waves Investigation and Monitoring, SWIM). SWIM has one nadir and five off-nadir beams to measure ocean surface waves. These near-nadir beams range from 0° to 10° at an interval of 2°. In this work, we investigated the performance of wave parameters derived from wave spectra measured by SWIM at off-nadir beams during the period 2020 to December 2022, e.g., incidence angles of 6°, 8° and 10°, which were collocated with the wave simulated by Simulating Waves Nearshore (SWAN). The validation of SWAN-simulated significant wave heights (SWHs) against National Data Buoy Center (NDBC) buoys of National Oceanic and Atmospheric Administration (NOAA) exhibited a 0.42 m root mean square error (RMSE) in the SWH. Our results revealed a RMSE of 1.02 m for the SWIM-measured SWH in the East Pacific Ocean compared with the SWH simulated by SWAN, as well as a 0.79 correlation coefficient (Cor) and a 1.17 squared error (Err) for the wave spectrum at an incidence angle of 10°, which are better than those (i.e., the RMSEs were > 1.1 m with Cors < 0.76 and Errs > 1.2) achieved at other incidence angles of SWH up to 14 m. This analysis indicates that the SWIM product is a relevant resource for wave monitoring over global seas. The collocated wave retrievals for more than 300 cases from Gaofen-3 (GF-3) synthetic aperture radar (SAR) images in China Seas were also used to verify the accuracy of SWIM-measured wave spectra. The energy of the SWIM-measured wave spectra represented by SWH was found to decrease with an increasing incidence angle in a case study. Moreover, the SWIM-measured wave spectra were most consistent with the SAR-derived wave spectra at an incidence angle of 10°, yielding a 0.77 Cor and 1.98 Err between SAR-derived and SWIM wave spectra under regular sea state conditions (SWH < 2 m). The error analysis indicates that the difference in SWH between SWIM at an incidence angle of 10° and SWAN has an increasing tendency with the growth in sea surface wind and sea state and it stabilizes to be 0.6 m at SWH > 4 m; however, the current and sea level have less influence on the uncertainties of the SWIM product.

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