China-France Oceanography Satellite Research Articles

Analysis of rain effects on China-France oceanography satellite scatterometer backscatter measurements

ABSTRACT The rotating fan beam scanning observation mechanism of China-France oceanography satellite (CFOSAT) scatterometer provides more comprehensive ocean measurements. However, its data quality is often impacted by rain contamination. In this study, we analysed the effect of rain on CFOSAT SCATterometer (CSCAT) backscatter measurements by collocating CSCAT data, the Integrated Multi-satellitE Retrievals (IMERG) rain data, and the wind reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF). The research findings indicate that, rain exhibits an intensification effect on backscatter at low wind speeds, but this effect gradually diminishes with increasing wind speeds, eventually transitioning to an attenuation effect at a wind speed between 10–20 m·s−1. At extremely high wind speeds above 20 m·s−1, the rain initially increases and then decreases CSCAT normalized radar cross section (σ 0) as the rain rate increases. The sensitivity of CSCAT σ 0 to rain rate decreases as the incidence angle increases. Its sensitivity to wind speed decreases as the rain rate increases. The azimuthal modulation becomes more pronounced with higher wind speeds and smaller incidence angles. However, rain weakens the asymmetry and anisotropy. Furthermore, the HH polarization exhibits a more pronounced susceptibility to rain compared to the VV polarization.

Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model

AbstractHigh‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.

Dual-Mode Sea Ice Extent Retrieval for the Rotating Fan Beam Scatterometer

Scatterometers are highlighted in polar applications, such as sea ice extent retrieval. There are currently three types of spaceborne scatterometer in operation, among which the rotating pencil beam scatterometer and the rotating fan beam scatterometer have similar rotating observation geometry, but different beam sampling. However, it is difficult to objectively evaluate the performance of the two rotating beam scatterometers using the obtained data. This is because there are significant differences in their system parameters, which in turn affects the objectivity of the evaluation. Considering the high flexibility of the rotating fan beam scatterometer, this study proposes a dual-mode sea ice extent retrieval method for the rotating fan beam scatterometer. The dual modes refer to the rotating fan beam mode (or full incidence mode) and the equivalent rotating pencil beam mode (or single incidence mode). The two modes share the same system and spatiotemporal synchronous backscatter measurements provide the possibility of objectively comparing the rotating pencil beam and rotating fan beam scatterometers. The comparison, validation, and evaluation of the dual-mode sea ice extent of China France Oceanography Satellite Scatterometer (CSCAT) were performed. The results indicate that the sea ice extent retrieval of the equivalent rotating pencil beam mode of the rotating fan beam scatterometer is realizable, and compared to the existing rotating pencil beam scatterometers (such as the OceanSat Scatterometer on ScatSat-1, OSCAT, on ScatSat-1, and the Hai Yang 2B Scatterometer, HSCAT-B), the derived sea ice extent is closer to that of Advanced Microwave Scanning Radiometer 2 (AMSR2). For the two modes of CSCAT, when compared to AMSR2, the sea ice extent of the CSCAT full incidence mode has smaller values of root mean squared error (RMSE), error-of-ice (EI), and ice edge location distance (LD) than those of the CSCAT single incidence mode. These suggest that the rotating fan beam scatterometer shows better observation abilities for sea ice extent than the rotating pencil beam scatterometers.

A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer

A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer

Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations

Assimilating observation data into wave models can significantly improve the numerical simulation accuracy of ocean waves. Traditionally, assimilation efforts have predominantly focused on significant wave height (SWH). However, the launch of the China-France Oceanography SATellite (CFOSAT)has facilitated the acquisition to obtain global-scale wave spectrum data, introducing a transformative dimension to assimilation methodologies. This novel wave spectrum data holds substantial potential for refining global wave data assimilation results. In our study, a series of numerical experiments were conducted employing CFOSAT data to systematically contrast the impact of wave spectrum assimilation with that of SWH assimilation on global wave simulations. The assimilating impacts of SWH assimilation and spectrum assimilation were validated against wave data retrieved from the National Data Buoy Center (NDBC) buoys and Jason-3 altimeter. The validation included SWH, mean wave period (MWP), dominant wave period (DWP), the direction from which the waves at the DWP are coming (DWD), and the wave spectrum. When comparing with the buoy measurements, spectrum assimilation demonstrated superior effectiveness in improving mean wave period (MWP) compared to SWH assimilation. The assimilation index for spectrum assimilation reached 12.21%, escalating to 15.78% when MWP exceeded or equaled 8 s. In contrast, for SWH, DWP and DWD, SWH assimilation performed better. Validation against Jason-3 altimeter data revealed that spectrum assimilation surpassed SWH assimilation in terms of overall improvement. Notably, SWH assimilation exhibited a more favorable impact when SWH values were <2.5 m. However, for SWH values greater than or equal to 2.5 m, spectrum assimilation outperformed SWH assimilation, yielding assimilation indices of 8.10% and 6.18% compared to buoys and Jason-3, respectively. Overall, this work illustrates that utilizing CFOSAT data for spectrum assimilation and SWH assimilation can enhance wave simulation accuracy under different conditions, which may point to promising applications in hurricane forecasting and disaster reduction research.

Seasonal variations in wave-induced stress over global ocean based on China France Oceanography Satellite

Seasonal variations in wave-induced stress over global ocean based on China France Oceanography Satellite

Polar Sea Ice Detection Using a Rotating Fan Beam Scatterometer

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2.

Retrieving Ocean Surface Winds and Waves from Augmented Dual-Polarization Sentinel-1 SAR Data Using Deep Convolutional Residual Networks

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.

Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation

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).

Towards a Consistent Wind Data Record for the CFOSAT Scatterometer

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.

Enhanced-resolution reconstruction for the China-France Oceanography Satellite scatterometer

The China-France Oceanography Satellite SCATterometer (CSCAT) can observe radar backscatter values on the same sea surface at multiple incidence angles, and is often used to estimate the ocean near-surface wind. However, CSCAT utilizes a novel scanning mechanism and the wind vector cell has a spatial resolution is 25 km or 12.5 km, which limit the study of high-resolution land and sea ice monitoring. To address this issue, this paper constructs a geometric model of the main lobe-to-ground projection relationship and generates the enhanced-resolution radar images. CSCAT data are applied to three main image reconstruction algorithms (SIR, AART, and MART), and experiments are performed in the Iceland and Hudson Bay, and verified by Sentinel-2 optical remote sensing data. The experiments show the geometric model for CSCAT improves the spatial resolution from traditional 25 km to 5 km, and the SIR-reconstructed images are characterized by higher accuracy and better suppression of noise than are those obtained with the AART and MART methods. Therefore, this study extends the application of domestic remote sensors and provides data support for high-resolution applications, such as land and sea ice monitoring.

Validation of Nadir SWH and Its Variance Characteristics from CFOSAT in China’s Offshore Waters

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.

Evaluation of CFOSAT Wave Height Data with In Situ Observations in the South China Sea

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.

Bayesian Sea Ice Detection Algorithm for CFOSAT

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.

Accurate mean wave period from SWIM instrument on-board CFOSAT

The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China–France Oceanography Satellite (CFOSAT) can provide wave spectra using its off-nadir beams. Although SWIM shows a reasonable performance for capturing spectral peak, the accuracy of mean wave periods (MWPs) computed directly from the SWIM spectra is not satisfying due to the high noise level of the spectra. SWIM can also provide good-quality simultaneous wind speed (U10) and significant wave height (SWH) like an altimeter. The MWP can also be estimated using a U10-SWH look-up table presented in previous studies. However, the accuracy of this method is also limited as the U10-SWH look-up table is only applicable for wind-sea-dominated conditions. The two MWP retrieval methods are independent of each other, and their error properties are complementary to each other. Therefore, this study further presents a merged MWP retrieval model combining the nadir U10-SWH and the MWP from the off-nadir spectrum of SWIM using a simple artificial neural network. After training against some buoy data, the model reaches unprecedented accuracy for MWP retrievals (RMSEs of ~0.36 s for zero up-crossing periods, ~0.41 s for mean periods, and ~ 0.60 s for energy periods), demonstrating the usefulness of SWIM in the studies of ocean waves.

Effects of rain on CFOSAT scatterometer measurements

The Ku-band scatterometer onboard China France Oceanography Satellite (CFOSAT) observes the sea surface with two conically scanning fan beams. Compared to the prior Ku-band pencil beam scatterometers, this innovative observing mechanism provides more independent backscatter measurements at varying incidence and azimuth angles, as such it brings challenges for the sea surface wind inversion, particularly under rainy conditions. In this paper, the rain effects on the CFOSAT SCATterometer (CSCAT) are investigated using the collocated numerical weather prediction (NWP) wind data and the Global Precipitation Measurement (GPM) microwave imager (GMI) rain data. Similar to the prior Ku-band or C-band scatterometers, the sensitivity of CSCAT radar backscatter to rain substantially varies with wind speed, radar polarization and incidence angle. However, due to the complex observation geometries, rain effects on the CSCAT retrieved winds is more complex than that of prior scatterometers, which may lead to a remarkable underestimation of CSCAT wind speed at high winds and heavy rain conditions. A simple simulation method is used to clarify the relation between the retrieved wind speed and the dependency of radar rain effects on the incidence angle. It is found that the backscatter measurements at low incidence angles, which are generally underestimated at high winds and heavy rainy conditions, have a larger influence on the wind inversion minimization, leading to much lower retrieved wind speeds than those of ECMWF and the pencil beam scatterometer (e.g., Haiyang-2B scattometer). Under low and moderate rain conditions though, a more compensated effect between low and high incidence angle measurements is found, leading to generally unbiased CSCAT high winds, in contrast to the generally underestimated pencil-beam scatterometer winds.

Numerical Simulation and Observational Data Analysis of Mesoscale Eddy Effects on Surface Waves in the South China Sea

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.

Validation of the CFOSAT Scatterometer Data With Buoy Observations and Tests of Operational Application to Extreme Weather Forecasts in Taiwan Strait

AbstractThe China‐France Oceanography Satellite (CFOSAT) launched in 2018 is equipped with the Chinese Scatterometer (CSCAT), which is designed to measure high‐precision sea surface wind fields in the global ocean. This study aims to validate the CFOSAT wind fields with moored buoy‐measured ground truth data from August 2019 to July 2021. The test area was chosen as the Taiwan Strait, which is an important navigation channel and rich fishery grounds in the East Asia. It is also a high wind speed area particularly during typhoons and cold air passages. Thus, the accurate wind forecasts are crucially needed to avoid capsizing and casualties. The validation results give the correlation coefficient (R2) of 0.92, the root mean square error (RMSE) of 1.75 m s−1, and the absolute deviation (AD) of 1.10 m s−1 for the wind speeds, and R2 of 0.96, the RMSE of 21.87°, and the AD of 1.17° for the wind directions. During cold air passages, the AD of wind speeds decreases to 1.03 m s−1, which is smaller than 1.17 m s−1 during typhoons. Using the CFOSAT wind as historical observation data for refining of forecast work was tested by model output statistics revised method. The test results show that the refined forecast results of wind fields are improved up to 4%–17%. Thus, the validation and test results obtained in this study indicate that updating satellite winds would have great contributions to refined forecasting of local sea surface winds.

Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks

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.

Preliminary Estimate of CFOSAT Satellite Products in Tropical Cyclones

The China France Oceanography Satellite (CFOSAT) was launched on October 29, 2018, and is equipped with two sensors: a space-borne rotating fan beam scatterometer (CSCAT) and a space-borne surface wave investigation and monitoring (SWIM) radar. The CSCAT is dedicated to monitoring sea surface winds (SSWs), and the SWIM radar is designed to measure surface ocean waves. In this study, CSCAT SSW products during global tropical cyclones (TCs) were validated using wind data from multiple sources, including European Center for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA5), cross-calibrated multi-platform (CCMP), soil moisture active passive (SMAP), WindSat, HY-2B, advanced scatterometer (ASCAT), and buoy wind. Wave parameters (significant wave height (SWH), dominant direction, and wavelength) were validated using wave data from ERA5, Jason3, and the HY-2B altimeter. The results show the following: 1) errors of the CFOSAT wind speed (WS) and SWH increase with the enhancement of TC intensity; 2) the WS accuracy of wind vector cells (WVCs) near the nadir is better than that at the nadir and the edge of the swath; 3) the wind direction (WD) correlation between the CSCAT and ERA5, ASCAT, and WindSat is more obvious at high WSs (>20 m/s) than at lower WSs (<6 m/s); 4) the accuracy of the SWIM off-nadir SWH product is lower than that of the nadir SWH; and 5) a triple collocation comparison among the CFOSAT SSW, ERA5 SSW, and WindSat SSW indicates that the CFOSAT SSW provides the best performance.