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The main purpose of this letter is to investigate the performance of the calibration of the Chinese civilian satellite carrying synthetic aperture radar (SAR), denoted as 1mC-SAR, which is a successor of the previous Gaofen-3 (GF-3) satellite. In total, more than 500 1mC-SAR images in co-polarization modes (vertical–vertical [VV] and horizontal–horizontal [HH]) are collocated with the wind products from Haiyang-2 (HY-2B/2C/2D) scatterometers in April – May 2023. The well-developed geophysical model function (GMF) CMOD7 and polarization ratio PR2011 in the C-band are used to simulate the normalized radar cross section (NRCS) employing the wind vector from HY-2. There are more than 2000 matchups for VV-polarization and more than 1000 matchups for HH-polarization. Comparison of the simulations and 1mC-SAR measurements yields a root mean squared error (RMSE) of 3.03 dB, a correlation coefficient (r) of 0.93, a scatter index (SI) of −0.14, and a bias of −2.22 dB for the VV-polarized NRCS, which is slightly better than the 3.68 dB RMSE, 0.90 r, −0.15 SI, and −3.08 dB bias of the HH-polarized NRCS. The variations in the difference in the VV-polarized NRCS (1mC-SAR measurements minus simulations) with respect to the wind speed and incidence angle are analysed. Despite the difference in the VV-polarization (i.e. <2 dB) at wind speeds of greater than 3 m s−1, the difference in the VV-polarization approaches 3 dB at several incidence angles, which needs to be further improved. [ABSTRACT FROM AUTHOR]

In this study, a machine learning approach is applied to wind speed retrieval from vertical-vertical (VV) polarized synthetic aperture radar (SAR) during tropical cyclones (TC). More than 2400 dual-polarized (VV and vertical-horizontal (VH)) Sentinel-1 (S-1) images acquired in interferometric wide (IW) and extra wide (EW) mode are collected in 2016–2022. Along-track observations from stepped-frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) are available for several images. The azimuthal cut-off wavelength (ACW) is derived from VV-polarized image and the Doppler centroid anomaly (DCA) is provided from Ocean (OCN) product. It is found that SFMR wind speed linearly relates with ACW and DCA. Following this rationale, a machine learning method, called eXtreme Gradient Boosting (XGBoost), is trained for constructing SAR wind speed retrieval geophysical model function (GMF) through abundant matchups from 2000 images collocated with wind speeds from soil moisture active passive (SMAP) microwave radiometer and SFMR, denoted as TCWIND2-S1. Then GMF TCWIND2-S1 is applied for more than 400 images and the validation against SMAP and SFMR products up to 70 m s−1 shows a 3.01 m s−1 root mean squared error (RMSE) with a 0.16 scatter index (SI) and a 0.92 correlation (COR). Retrieval is also compared with CyclObs wind products derived from more than 400 dual-polarized images, yielding a 2.66 m s−1 RMSE with a 0.19 SI and a 0.93 COR. Our work provides an approach for VV-polarized SAR wind retrieval at wind speed ranged from 0 to 70 m s−1 during TCs. This work is conveniently adopted for TerraSAR-X without operating at cross polarization channel. [ABSTRACT FROM AUTHOR]

In this letter, we present a validation of the wind speed retrieval from data from the calibration microwave radiometer onboard the Haiyang-2B (HY-2B) satellite against the observations from the stepped-frequency microwave radiometer (SFMR) onboard hurricane aircraft of the National Oceanic and Atmospheric Administration (NOAA) during 50 tropical cyclones (TCs). These data were collected during the cyclone season for the period from 2019 to 2022. The validation yields a root mean square error (RMSE) of 2.99 m s−1, a correlation coefficient (r) of 0.69, and a scatter index (SI) of 0.21 for the wind speed at wind speeds of <25 m s−1, which are worse than those achieved through the comparison with the HY-2B scatterometer, i.e. an RMSE of 1.15 m s−1, an r value of 0.96, and an SI of 0.11. This is probably caused by the fact that the brightness temperature measurement from the calibration microwave radiometer is likely affected by whitecaps caused by wave breaking; however, whitecaps have less influence on the sea surface backscattering signal under a regular sea state. At wind speeds of >25 m s−1, the accuracy is reduced to an RMSE of 2.19 m s−1, an r value of 0.75, and an SI of 0.07. In the presence of rain, significant distortion was observed, i.e. a variation of 3 m s−1 for a rain rate of 15 mm hr−1. This behaviour was also observed in the analysis of the HY-2B scatterometer product at low-to-moderate wind speeds. In this sense, it is believed that correction associated with rain has to be conducted in wind retrieval from HY-2B data. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state. [ABSTRACT FROM AUTHOR]

In this study, the feasibility of current retrieval from Sentinel-1 (S-1) synthetic aperture radar (SAR) in the radar look/range direction is investigated. S-1 Ocean (OCN) products acquired in interferometric wide (IW) mode in the regions with the western boundary current, i.e., the western Pacific and western Atlantic, are collected for the period from 2020 to 2022, which are collocated with the current field from HYbrid Coordinate Ocean Model (HYCOM). The OCN wind, HYCOM current, and Stokes drift estimated from the OCN wave parameters are geometric projected to be the range direction. In addition, the Doppler centroid anomaly (DCA) is estimated using the difference between the radar return Doppler frequency and the predicted Doppler shift, which are derived from the OCN products. The dependences of the upper ocean dynamics in the range direction on the DCA are studied, and it is found that the range Stokes drift, wind speed, and current speed are linearly related to the DCA. Based on deep learning, denoted as multi-layer perceptron, a range current retrieval algorithm from the OCN product is developed using two-thirds of the collocated dataset, and the root mean square error (RMSE) of the range current speed converges to 0.15 ms−1. In particular, the wave-induced surface Stokes drift is considered in the process. Comparison of one-third of the dataset yields an RMSE of 0.14 ms−1 for the range current speed, a correlation coefficient (r) of 0.85, and a bias of −0.001 ms−1. Validation against several moored buoys shows an RMSE of 0.12 ms−1 with a r of 0.74 and a bias of −0.01 ms−1. Under this circumstance, it is believed that the algorithm used in this study is applicable for range current retrieval from the S-1 OCN product. [ABSTRACT FROM AUTHOR]

The strong wind-induced current and sea level have influences on the wave distribution in a tropical cyclone (TC). In particular, the wave–current interaction is significant in the period in which the TC passed the mesoscale eddy. In this study, the wave fields of Typhoon Chan-hom (2015) are hindcastly simulated using a coupled oceanic model that utilizes a nested triangle grid, i.e., the finite-volume community ocean model-simulating waves nearshore (FVCOM-SWAVE) model. The forcing wind field is composited from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data and the simulation using a parametric Holland model, denoted as H-E. The open boundary fields include tide data from TPOX.5 and the hybrid coordinate ocean model (HYCOM) global datasets, including sea surface temperature (SST), sea surface salinity, sea surface current, and sea level data. The simulated oceanic parameters (e.g., the significant wave height, SWH) are validated against the measurements from the Jason-2 altimeter, yielding a root mean square error (RMSE) of 0.58 m for the SWH, a correlation (COR) coefficient of 0.94, and a scatter index (SI) of 0.23. Similarly, the simulated SSTs are compared with the remote sensing products of the remote sensing system (REMSS) and the measurements from Argos, yielding an RMSE of <0.8 °C, a COR of >0.95, and an SI of <0.04. The significant zonal asymmetry of the wave distribution along the typhoon track is observed. The Stokes drift is calculated from the FVCOM-SWAVE simulation results, and then the contribution of the Stokes transport is estimated using the Ekman–Stokes numbers. It is found that the ratio of the Stokes transport to the total net transport can reach >80% near the typhoon center, and the ratio is reduced to approximately <20% away from the typhoon center, indicating that Stokes transport is an essential aspect in the water mixing during a TC. The mesoscale eddies are detected by the sea level anomalies (SLA) fusion data from AVISO. It is found that the significant wave heights, Stokes drift, and Stokes transport inside the eddy area were higher than those outside the eddy area. These parameters inside the cold mesoscale eddies were higher than t inside the warm mesoscale eddies. Otherwise, the SST mainly increased within the cold mesoscale eddies area, while decreased within the warm mesoscale eddies area. The influence of mesoscale eddies on the SST was in proportion to the eddy radius and eddy EKE. [ABSTRACT FROM AUTHOR]

A wind field reconstruction method for dual-polarized (vertical-vertical [VV] and vertical-horizontal [VH]) Sentinel-1 (S-1) synthetic aperture radar (SAR) images collected during tropical cyclones (TCs) that does not require external information is proposed. Forty S-1 images acquired in interferometric-wide (IW) and extra-wide (EW) modes during the Satellite Hurricane Observation Campaign in 2015–2022 were collected. Stepped-frequency microwave radiometer (SFMR) observations made onboard the National Oceanic and Atmospheric Administration's hurricane aircraft are available for 13 images. The geophysical model functions, namely VV-polarized C-SARMOD and cross-polarized S-1 IW/EW mode wind speed retrieval model after noise removal (S1IW.NR/S1EW.NR), were employed to invert the wind fields from the collected images. TC wind fields were reconstructed based on SAR-derived winds, enhancing TC intensity representation in the VV-polarized SAR retrievals and minimizing the error of the VH-polarized SAR retrievals at the sub-swath edge. The wind speeds retrieved from the SAR IW image were validated against the remote-sensing products from the soil moisture active passive (SMAP) radiometer, yielding a root mean squared error (RMSE) of approximately 4.3 m s−1, which is slightly smaller than the RMSE (4.4 m s−1) for the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Sea (IFREMER). However, the CyclObs wind product has better performance than the approach proposed in this paper for the S-1 EW mode. Moreover, the RMSE of the wind speed between SAR-derived wind speed obtained using the proposed approach and the CyclObs wind product is within 3 m s−1 in all flow directions clockwise relative to north centered on the TC's eye. This study provides an alternative method for TC wind retrieval from dual-polarized S-1 images without suffering saturation problem and external information; however, the pattern of the wind field around the TC's eye needs to be further improved, especially at the head and back of the TC's eye. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of our work is to improve the modulation transfer function (MTF) of velocity bunching by the quad-polarized GF-3 synthetic aperture radar (SAR). In total, more than 10,000 Gaofen-3 (GF-3) images acquired in wave mode (WV) in quad-polarization are collocated with ocean waves simulated from WAVEWATCH-III (WW3) model and ocean currents from the HYbrid Coordinate Ocean Model (HYCOM). The validation of WW3-simulated significant wave height (SWH) against the measurements from National Data Buoy Center (NDBC) of National Oceanic and Atmospheric Administration (NOAA) yields a 0.48 m root-mean-square error (RMSE) of SWH with a 0.88 correlation coefficient (r). The winds are inverted using the geophysical model function (GMF), denoted as CSARMOD-GF, which is specially for the re-calibrated GF-3 SAR in vertical-vertical (VV) polarization. The azimuthal cut-off wavelength λc caused by velocity bunching is theoretically calculated by wave spectrum from WW3 model and practically estimated from SAR intensity image, respectively. It is found that the velocity bunching is independent with polarization; however, the bias (theoretic minus SAR-derived λc) increases with sea state growing and has no obvious relationship with current. Following this rationale, the MTF of velocity bunching is empirically constructed by including the term of wind speed and the analysis shows a 3.76 m RMSE with a 0.92 r through comparing the SAR-derived λc in VV polarization and simulated values. Under this circumstance, the accuracy of wave retrieval is anticipated to be improved when using the proposed MTF of velocity bunching. [ABSTRACT FROM AUTHOR]

In this work, three types of machine learning algorithms are applied for synthetic aperture radar (SAR) wind retrieval in tropical cyclones (TCs), and the optimal method is confirmed. In total, 30 Sentinel-1 (S-1) images in dual-polarization (vertical–vertical [VV] and vertical–horizontal [VH] were collected during the period from 2016 to 2021, which were acquired in interferometric-wide and extra-wide modes with pixels of 10 m and 40 m, respectively. More than 100,000 sub-scenes with a spatial coverage of 3 km are extracted from these images. The dependences of variables estimated from sub-scenes, i.e., VV-polarized and VH-polarized normalized radar cross-section (NRCS), as well as the azimuthal wave cutoff wavelength, on wind speeds from the stepped-frequency microwave radiometer (SFMR) and the soil moisture active passive (SMAP) radiometer are studied, showing the linear relations between wind speed and these three parameters; however, the saturation of VV-polarized NRCS and the azimuthal wave cutoff wavelength is observed. This is the foundation of selecting input variables in machine learning algorithms. Two-thirds of the collocated dataset (20 images) are used for training the process using three machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), Multi-layer Perceptron, and K-Nearest Neighbor, and the coefficients are fitted after training completion through 20 images collocated with SFMR and SMAP data. Another 10 images are taken for validation up to 70 m/s, yielding a 2.53 m/s root mean square error (RMSE) with a 0.96 correlation and 0.12 scatter index (SI) using XGBoost. The result is better than the >5 m/s error achieved using the existing cross-polarized geophysical model function and the other two machine learning algorithms; moreover, the comparison between wind retrievals using XGBoost and Level-2 CyclObs products shows about 4 m/s RMSE and 0.18 SI. This suggests that the machine learning algorithm XGBoost is an effective method for inverting the TC wind field utilizing SAR measurements in dual-polarization. [ABSTRACT FROM AUTHOR]

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models. [ABSTRACT FROM AUTHOR]

Heavy rain is associated with strong winds and extreme waves in a tropical cyclone (TC). In this letter, a practical algorithm for rain rate retrieval in TCs is proposed through 24 dual-polarized [vertical-vertical (VV) and vertical-horizontal (VH)] Sentinel-1 (S-1) synthetic aperture radar (SAR) images acquired in interferometric-wide (IW) swath mode, in which 13 images are collocated with the observations from stepped-frequency microwave radiometers (SFMRs). TC winds are directly obtained from VH-polarized images utilizing the geophysical model function (GMF) S-1 IW mode wind speed retrieval model after noise removal (S1IW.NR). The normalized radar cross section (NRCS) at VV-polarization channel is simulated using GMF CMOD5N and VH-polarized SAR wind. It is found that the difference between the simulated NRCSs and measurements from SAR is linearly related to the rain rate and oscillates with the incidence angle. Following this finding, an empirical algorithm for SAR rain rate retrieval is developed, denoted as CRAIN2_S1, which considers the influence of the radius of the maximum wind speed. The proposed algorithm is applied to 11 images in the dataset, and the validation of the rain rate (up to 35 mm/hr) against the products from global precipitation measurements (GPMs) has a root mean square error (RMSE) of 1.74 mm/hr, a correlation coefficient of 0.92 and a scatter index (SI) of 0.29. Collectively, it is concluded that the algorithm CRAIN2_S1 can be practically applied for dual-polarized SAR rain rate retrieval without any external information. [ABSTRACT FROM AUTHOR]

Under global climate change, the characteristics of oceanic dynamics are gradually beginning to change due to melting sea ice. This study focused on inter-annual variation in waves and mesoscale eddies (radius > 40 km) in the Arctic Ocean from 1993 to 2021. The waves were simulated by a numerical wave model, WAVEWATCH-III (WW3), which included a parameterization of ice–wave interaction. The long-term wind data were from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), and current and sea level data from the HYbrid Coordinate Ocean Model (HYCOM)were used as the forcing fields. The simulated significant wave heights (SWHs) were validated against the 2012 measurements from the Jason-2 altimeter, yielding a 0.55 m root mean square error (RMSE) with a 0.95 correlation (COR). The seasonal variation in WW3-simulated SWH from 2021 to 2022 showed that the SWH was the lowest in summer (July and August 2021) and highest in winter (November 2021 to April 2022). This result indicates that parts of the Arctic could become navigable in summer. The mesoscale eddies were identified using a daily-averaged sea level anomalies (SLA) product with a spatial resolution of a 0.25° grid for 1993−2021. We found that the activity intensity (EKE) and radius of mesoscale eddies in the spatial distribution behaved in opposing ways. The analysis of seasonal variation showed that the increase in eddy activity could lead to wave growth. The amplitude of SWH peaks was reduced when the Arctic Oscillation Index (AOI) was <−1.0 and increased when the AOI was >0.5, especially in the case of swells. The amplitude of SWH oscillation was low, and the EKE and radius of eddies were relatively small. Although the radius and EKE of eddies were almost similar to the AOI, the waves also influenced the eddies. [ABSTRACT FROM AUTHOR]

In this paper, an essential modulation transfer function (MTF) on synthetic aperture radar (SAR), denoted as tilt modulation, is improved through five vertical–vertical (VV) polarized Gaofen-3 (GF-3) SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode that were captured during the 2017 tropical cyclone (TC) season in the western Pacific Ocean. These images are reprocessed to be Level-1B products and collocated with the wind fields from Global and Regional Assimilation and Prediction System – Typhoons Model. More than 9,000 matchups are exploited for studying the dependence of tilt on the wind speed and incidence angle. It is found that quantitative tilt MTF (herein denoted as the derivative scattering coefficient), which is proportional to first-order derivative normalized radar cross-section with respect to incidence angle, is inversely related with the incidence angle between 14° and 46°. In addition, the wind speed has an obvious influence on the derivative scattering coefficient due to each scattering facet essentially being modulated by strong wind-induced long-waves, whereas traditional tilt MTF does not conform to the actual situation. However, the sensitivity of wind speed on the derivative scattering coefficient weakens at larger incidence angles (>40°). Following this rationale, the empirical tilt MTF in VV polarization has been developed. The reanalysis results indicate that the values from empirical tilt MTF could depict the role of wind. The improvement herein could overcome the limitation in tilt MTF, namely, the lack of a strong wind's influence, and it is expected to be implemented for SAR wave retrieval in TCs. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km2. [ABSTRACT FROM AUTHOR]

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming. [ABSTRACT FROM AUTHOR]

The main purpose of our work is to investigate the performance of wave breaking and its effect on wave retrieval in data acquired from the Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) at C-band and the German TerraSAR-X (TS-X) at X-band. The SAR images available for this study included 140 GF-3 images acquired in quad-polarization strip (QPS) mode and 50 dual-polarized (vertical-vertical (VV) and horizontal-horizontal (HH)) TS-X images acquired in stripmap (SM) mode. Moreover, these images were collocated with the waves simulated by the numeric WAVEWATCH-III (WW3) (version 5.16) model and HYbrid Coordinate Ocean Model (HYCOM) current. In particular, a few images covered the moored buoys monitored by the National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA). The comparison between the WW3-simulated results and the significant wave heights (SWHs) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA-5) showed that the correlation coefficient (COR) was 0.4–0.6 with a root mean squared error (RMSE) of about 0.2 m at SWHs of 0–4 m. The winds were inverted using VV-polarized geophysical model functions (GMFs), e.g., CSARMOD-GF for the GF-3 images and XMOD2 for the TS-X images. The Bragg resonant roughness in the normalized radar cross section (NRCS) was simulated using a radar backscattering model and the SAR-derived wind, WW3-simulated wave parameters, and HYCOM current. Then, the contribution of the non-polarized (NP) wave breaking to the SAR data was estimated by the VV-polarized NRCS, the HH-polarized NRCS, and the polarization ratio (PR) of the co-polarized Bragg resonant components in the NRCS. Because co-polarized Bragg resonant components in the NRCSs have poor results, due to the saturation for wind speeds greater than 20 m/s, the analysis of wave breaking is excluded at such conditions. The results revealed that the backscattering signal in the C-band was more sensitive to wave breaking than the backscattering signal in the X-band. Interestingly, the ratio had a linear correlation with wind speed. Moreover, the variation in the bias (inverted SWH minus WW3 simulation) showed that the bias increased as the wind speed (>8 m/s) and whitecap coverage (>0.005) increased. Following this rationale, wave retrieval during tropical cyclones should consider the influence of wave breaking. [ABSTRACT FROM AUTHOR]

The strong winds in tropical cyclones (TCs) are commonly retrieved from cross-polarized SAR images using a geophysical model function (GMF). However, the accuracy of wind retrieval in cross-polarization is significantly reduced at the edges of sub-swaths. In this study, a novel approach to TC wind retrieval from VV polarized SAR images is proposed based on using the azimuthal cutoff wavelength to represent the effect of velocity bunching. A total of 12 dual-polarized (VV and VH) Sentinel-1 (S-1) images acquired in the interferometric wide (IW) mode were used, five of which were collocated with measurements taken by the Stepped-Frequency Microwave Radiometer (SFMR) on board an NOAA aircraft. The SAR-based azimuthal cutoff wavelengths were found to be linearly related to the SFMR wind speeds. Based on this finding, an empirical GMF for TC wind speed retrieval from VV S-1 images was constructed. The inversion results from seven images using this approach were validated against the wind products from the Advanced Scatterometer and the European Center for Medium-Range Weather Forecasts. The RMSE of the wind speed was 2.15 m s−1 and the correlation coefficient (COR) was 0.83 at wind speeds of less than 25 m s−1, while the RMSE was 2.66 m s−1 and the COR was 0.97 when compared with wind retrieval using the VH-polarized GMF S1IW.NR at wind speeds greater than 25 m s−1. The proposed algorithm performs well and has two advantages: (1) it is not subject to the saturation problem of the VV backscattering signal and (2) the discontinuity of the retrieval results obtained using VH GMF at the edges of sub-swaths is improved. [ABSTRACT FROM AUTHOR]

The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone's eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone's eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a <2 m s−1 and >0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s−1. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s−1 with a 0.45 COR ranged from 330° to 360°. [ABSTRACT FROM AUTHOR]

We found that significant errors occurred when diurnal data instead of diurnal–nocturnal data were used to calculate the daily sea-air CO2 flux (F). As the errors were mainly associated with the partial pressure of CO2 in seawater (pCO2w) and the sea surface temperature (SST) in the control experiment, pCO2w and SST equations were established, which are called the nocturnal effect of the CO2 flux. The root-mean-square error between the real daily CO2 flux (Freal) and the daily CO2 flux corrected for the nocturnal effect (Fcom) was 11.93 mmol m−2 d−1, which was significantly lower than that between the Freal value and the diurnal CO2 flux (Fday) (46.32 mmol m−2 d−1). Thus, the errors associated with using diurnal data to calculate the CO2 flux can be reduced by accounting for the nocturnal effect. The mean global daily CO2 flux estimated based on the nocturnal effect and the sub-regional pCO2w algorithm (cor_Fcom) was −6.86 mol m−2 y−1 (September 2020–August 2021), which was smaller by 0.75 mol m−2 y−1 than that based solely on the sub-regional pCO2w algorithm (day_Fcom). That is, compared with day_Fcom, the global cor_Fcom value overestimated the CO2 sink of the global ocean by 10.89%. [ABSTRACT FROM AUTHOR]

The goal of this study was to investigate the performance of a spectral-transformation wave retrieval algorithm and confirm the accuracy of wave retrieval from C-band Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) images. More than 200 GF-3 SAR images of the coastal China Sea and the Japan Sea for dates from January to July 2020 were acquired in the Quad-Polarization Strip (QPS) mode. The images had a swath of 30 km and a spatial resolution of 8 m pixel size. They were processed to retrieve Significant Wave Height (SWH), which is simulated from a numerical wave model called Simulating WAves Nearshore (SWAN). The first-guess spectrum is essential to the accuracy of Synthetic Aperture Radar (SAR) wave spectrum retrieval. Therefore, we proposed a wave retrieval scheme combining the theocratic-based Max Planck Institute Algorithm (MPI), a Semi-Parametric Retrieval Algorithm (SPRA), and the Parameterized First-guess Spectrum Method (PFSM), in which a full wave-number spectrum and a non-empirical ocean spectrum proposed by Elfouhaily are applied. The PFSM can be driven using the wind speed without calculating the dominant wave phase speed. Wind speeds were retrieved using a Vertical-Vertical (VV) polarized geophysical model function C-SARMOD2. The proposed algorithm was implemented for all collected SAR images. A comparison of SAR-derived wind speeds with European Center for Medium-Range Weather Forecasts (ECMWF) ERA-5 data showed a 1.95 m/s Root-Mean-Squared Error (RMSE). The comparison of retrieved SWH with SWAN-simulated results demonstrated a 0.47 m RMSE, which is less than the 0.68 m RMSE of SWH when using the PFSM algorithm. [ABSTRACT FROM AUTHOR]

Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China's seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states. [ABSTRACT FROM AUTHOR]

In this study, the non-Bragg (NB) scattering due to breaking waves as measured by the C-band synthetic aperture radar (SAR) is investigated using more than 300 Gaofen-3 (GF-3) SAR images, which were acquired in quad-polarization stripmap (QPS) mode, that is, co-polarization [vertical–vertical (VV) and horizontal–horizontal (HH)] and cross-polarization [vertical–horizontal (VH) and horizontal–vertical (HV)]. First, the quality of SAR-based wind estimation is checked against the Haiyang-2B (HY-2B) scatterometer and European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), indicating a wind speed accuracy of 1.62 m s−1 root-mean-square error (RMSE) and a 0.89 correlation. Then, the SAR-derived wind and HYbrid Coordinate Ocean Model (HYCOM) sea surface current are used to simulate Bragg resonant roughness. The non-polarized (NP) wave breaking contribution σwb on co-polarized SAR-measured normalized radar cross section (NRCS) σ0 is studied, which is derived using two methods: an approach of the Bragg theory and empirical function. Numerical simulations are contrasted with actual SAR measurements: they show that the theoretical-based approach provides accurate enough simulations of the NP contribution, especially at the HH-polarization channel. To deeply understand the behavior of sea surface scattering under breaking conditions, the third-generation WAVEWATCH-III (WW3) model is used to simulate wake-breaking parameters, i.e. whitecap coverage (WCC), whitecap foam thickness (WCT) and whitecap breaking height (WCH), which are then collocated with SAR images. The difference between simulated co-polarized NRCS and the measured one versus sea surface dynamics parameters (i.e. SAR-derived wind speed, HYCOM sea surface speed, and WW3-simulated significant wave height) shows that NP enhances HH-polarized backscattering, while it damps the VV-polarized backscattering. In addition, the contribution of σwb could be ignored for WCC and WCT larger than 15 × 10−3 and 40 × 10−3 m, respectively. Moreover, the ratio reduces with the increasing WCH greater than 2.0 m; in particular, the ratio likely remains to be 0.1 as WCH is greater than 2.5 m. Generally, the HH-polarized backscattering is relatively sensitive with the wave-breaking parameters; however, this behavior has to be further studied utilizing buoy-measured wave breaking data. [ABSTRACT FROM AUTHOR]

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to quantitatively examine the characteristics of suspended sediment on synthetic aperture radar (SAR) observations of sea roughness. In this work, seven Sentinel-1 (S-1) SAR images in 2019 acquired in the Interferometric Wide-swath (IW) mode at vertical-vertical (VV) polarization channel were collected, where Yangtze River freshwater dominants and suspended sediment concentration (SSC) are higher in continental shelf of East China Sea (ECS). These images were collocated with waves simulated from WAVEWATCH-III (WW3) and SSC inverted from the Haiyang-1C (HY-1C) Coastal Zone Imager (CZI) image. The comparison of WW3-simulated SWH yielded a 0.45-m root-mean-square error (RMSE) with a 0.78 correlation (COR) when comparing the simulations with the measurements from Haiyang-2B (HY-2B). The SAR-measured normalized radar cross-section (NRCS) is linearly related with the HY-1 C SSC at SSC > 1200 mg l–1, which is caused by the change of dielectric constant of sea water and viscosity. Moreover, the NRCS is simulated by the three-scale radar backscattering model. It is generally found that HY-1 C SSC enhances the NRCS in difference between simulations and observations up to 8 dB at SSC of 2000 mg l–1, which could distort the accuracy of SAR wind retrieval. [ABSTRACT FROM AUTHOR]

This work focused on the rain-induced characteristics of synthetic aperture radar (SAR) at the C- and X-bands. Six SAR images were collected in tropical cyclones: four C-band dual-polarized Sentinel-1 (S-1) images during the 2016 hurricane season and two vertical–vertical) polarized TerraSAR-X (TS-X) images of Hurricane Sandy (2012). Wind speeds were retrieved from the S-1 images at the vertical–horizontal polarization channel, and winds from the TS-X images were obtained from the Hurricane Research Division of the National Oceanic and Atmospheric Administration. Simultaneously, Tropical Rainfall Measuring Mission (TRMM) rain rates, current data from the HYbrid Coordinate Ocean Model and wave spectra simulated by the WAVEWATCH-III (WW3) model were collocated. The three-scale backscattering model was employed to simulate the normalized radar cross-section (NRCS)without considering the rain effect, yielding a 2.21-dB root mean square errorand 2.23-dB for S-1 and TS-X, respectively. The difference between model-simulated and observed NRCS was analysed using the TRMM rain rate. The results indicate a linear relationship of the difference at the X-band with the TRMM rain rate, while the difference at the C-band exhibited a 'V' relationship with the TRMM rain rate. It was also discovered that the difference at the X-band decreased with increasing wind speed, while the difference at the C-band increased with increasing wind speed. [ABSTRACT FROM AUTHOR]

The spatial resolution of Chinese Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) image, a pixel size of 500 m at azimuth/flight direction for Global Observation (GLO) and 100 m for Wide ScanSAR (WSC) modes, is relatively coarse, and waves shorter than 100 m are undetectable. In this study, SAR images collected under typhoon conditions by the GF-3 mission are exploited for wave retrieval purposes. In detail, six imagery collected during 2017 and 2018 over the China Seas captured four typhoons. The GLO and WSC modes images are collocated with simulated wave fields using a numeric wave model, called WAVEWATCH-III (WW3) and are processed using a machine learning method to retrieve sea surface waves. In this work, the criterion for the training completion in the process of machine learning is set as 0.15 m of Root mean Square Error (RMSE) for the Significant Wave Height (SWH). Then, the retrieved SWH is compared with measurements collected by the Jason-2 altimeter, showing a 0.61 m RMSE. The proposed algorithm is shown to outperform empirical approaches developed to retrieve the SWH using GF-3 SAR imagery acquired in the GLO and WSC imaging modes. [ABSTRACT FROM AUTHOR]

If the CO SB 2 sb flux is negative, it means that CO SB 2 sb enters the ocean from the atmosphere, i.e., the ocean is the sink of CO SB 2 sb **. The added sentence is: **If the CO SB 2 sb flux is positive, it means that CO SB 2 sb enters the atmosphere from the ocean, i.e., the ocean is the source of CO SB 2 sb . We want to add the following two sentences on CO SB 2 sb source and sink to **Introduction**, **Paragraph 2**, after the phrase: **At present, the sea-air CO SB 2 sb flux can be measured directly using the eddy correlation method. [Extracted from the article]

In this study, we simulated typhoon waves in the shallow waters around the Zhoushan Islands using the WaveWatch-III (WW3) model version 5.16, the latest version released by the National Oceanic and Atmospheric Administration. Specifically, we used in-situ measurements to evaluate the performance of seven packages of input/dissipation source terms in the WW3 model. We forced the WW3 model by wind fields derived from a combination of the parametric Holland model and high-resolution European Center for Medium-Range Weather Forecasts (ECMWF) wind data in a 0.125° grid, herein called H-E winds. We trained the H-E winds by fitting a shape parameter B to buoy-measured observations, which resulted in a smallest root mean square error (RMSE) of 3 m s−1 for B, when treated as a constant 0.4. Then, we applied the seven input/dissipation terms of WW3, labelled ST1, ST2, ST2+STAB2, ST3, ST3+STAB3, ST4, and ST6, to simulate the significant wave height (SWH) up to 5 m during typhoons Fungwong and Chan-hom around the Zhoushan Islands. We then compared the SWHs of the simulated waves with those measured by the in-situ buoys. The results indicate that the simulation using ST2 performs best with an RMSE of 0.79 m for typhoon Fung-wong and an RMSE of 1.12 m for typhoon Chan-hom. Interestingly, we found the simulated SWH results to be relatively higher than those of the observations in the area between Hangzhou Bay and the Zhoushan Islands. This behavior is worthy of further investigation in the future. [ABSTRACT FROM AUTHOR]

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode. [ABSTRACT FROM AUTHOR]

The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode during the summer of 2017 from the China Sea, which includes the typhoons Noru, Doksuri and Talim. These images were collocated with wind simulations at 0.12° grids from a numeric model, called the Regional Assimilation and Prediction System-Typhoon model (GRAPES-TYM). Recent research shows that GRAPES-TYM has a good performance for typhoon simulation in the China Sea. Based on the dataset, the dependence of wind speed and of radar incidence angle on normalized radar cross (NRCS) of VH-polarization GF-3 SAR have been investigated, after which an empirical algorithm for wind speed retrieval from VH-polarization GF-3 SAR was tuned. An additional four VH-polarization GF-3 SAR images in three typhoons, Noru, Hato and Talim, were investigated in order to validate the proposed algorithm. SAR-derived winds were compared with measurements from Windsat winds at 0.25° grids with wind speeds up to 40 m/s, showing a 5.5 m/s root mean square error (RMSE) of wind speed and an improved RMSE of 5.1 m/s wind speed was achieved compared with the retrieval results validated against GRAPES-TYM winds. It is concluded that the proposed algorithm is a promising potential technique for strong wind retrieval from cross-polarization GF-3 SAR images without encountering a signal saturation problem. [ABSTRACT FROM AUTHOR]

Active microwave remote sensing of hurricane-strength wind is a challenging task due to the saturation of col-polarization backscattering signal under such condition. Here we take advantage of the fact that wind sea wave growth does not saturate at high wind and the intrinsic relationship among wind-wave triplets (sea surface wind speed, significant wave height, and peak wave period) within a tropical storm to derive the wind speed. Three Sentinel-1 (S-1) and nine RADARSAT-2 (R-2) C-band synthetic aperture radar (SAR) images acquired between 20 and 40 m/s winds are collected in this study. The S-1 and R-2 SAR-derived winds are compared with those measured by coincident National Oceanic and Atmospheric Administration Stepped-Frequency Microwave Radiometer (SFMR) and simulated by Symmetric Hurricane Estimates for Wind (SHEW) model. Validations against SFMR winds for S-1 show Root-Mean-Square Error (RMSE) of 1.7 m/s with a 0.2 m/s bias at the left side of cyclone centers and RMSE of 2.9 m/s RMSE with a 0.56 bias at the backside of cyclone centers. R-2 SAR-derived winds against SHEW model results show a RMSE of 2.4 m/s with a 0.3 m/s bias and 2.6 m/s with a 0.35 m/s bias at the right side and the left side of cyclone centers, while the RMSE is 3.9 m/s with a 0.1 m/s bias at the backside of cyclone centers. The wave-information-based wind retrieval method works well at the left side and right side, but less accurately at the backside of a tropical storm when wind wave and swell are mixed. [ABSTRACT FROM AUTHOR]