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Relevant bibliographies by topics / Cfosat

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Journal articles

Academic literature on the topic 'Cfosat'

Author: Grafiati

Published: 20 April 2024

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Journal articles on the topic "Cfosat"

1

Xu, Jingwei, Huanping Wu, Ying Xu, et al. "Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters." Remote Sensing 15, no. 4 (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 h

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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 (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 buoy

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3

Ye, Haijun, Junmin Li, Bo Li, et al. "Evaluation of CFOSAT Scatterometer Wind Data in Global Oceans." Remote Sensing 13, no. 10 (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 tha

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4

DONG Xiaolong, ZHU Di, LIN Wenming, et al. "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 (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

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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 (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

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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 (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

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9

Li, Bo, Junmin Li, Junliang Liu, et al. "Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks." Remote Sensing 14, no. 3 (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 10

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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 (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 typh

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