Search Results (46)

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the optical data. To achieve near real-time compensation, a novel jitter error estimation and correction method based on multi-source attitude data fusion is proposed in this paper. By fusing the measurement data from star sensors and gyroscopes, satellite attitude parameters containing jitter errors are precisely resolved. The jitter component of the attitude parameter is extracted using the fitting method with the optimal sliding window. Then, the jitter error model is established using the least square solution and spectral characteristics. Subsequently, using the imaging geometric model and stable resampling, the optical remote sensing image with jitter distortion is corrected. Experimental results reveal a jitter frequency of 0.187 Hz, matching the OCTS rotation period, with yaw, roll, and pitch amplitudes quantified as 0.905”, 0.468”, and 1.668”, respectively. The registration accuracy of the multispectral images from the Coastal Zone Imager improved from 0.568 to 0.350 pixels. The time complexity is low with the single-layer linear traversal structure. The proposed method can achieve on-orbit near real-time processing and provide accurate attitude parameters for on-orbit geometric processing of optical satellite image data. Full article

Haiyang 2B (HY-2B), the second Chinese ocean dynamic environment monitoring satellite, has been operational for nearly six years. The scanning microwave radiometer (SMR) onboard HY-2B provides global sea surface temperature (SST) observations. Comprehensive validation of these data is essential before they can be effectively applied. This study evaluates the operational SST product from the SMR, covering the period from 1 January 2019 to 31 August 2024, using direct comparison and extended triple collocation (ETC) methods. The direct comparison assesses bias and root mean square error (RMSE), while ETC analysis estimates the random error of the SST measurement systems and evaluates their ability to detect SST variations. Additionally, the spatial and temporal variations in error characteristics, as well as the crosstalk effects of sea surface wind speed, columnar water vapor, and columnar cloud liquid water, are analyzed. Compared with iQuam SST, the total RMSE of SMR SST for ascending and descending passes are 0.88 °C and 0.85 °C, with total biases of 0.1 °C and −0.08 °C, respectively. ETC analysis indicates that the random errors for ascending and descending passes are 0.87 °C and 0.80 °C, respectively. The SMR’s ability to detect SST variations decreases significantly at high latitudes and near 10°N latitude. Error analysis reveals that the uncertainty in SMR SSTs has increased over time, and the presence of crosstalk effects in SMR SST retrieval has been confirmed. Full article

Sea ice thickness is an important component of the Arctic environment, bearing crucial significance in investigations pertaining to global climate and environmental changes. This study employs data from the HaiYang-2B satellite altimeter (HY-2B ALT) for the estimation of Arctic Sea ice thickness from November 2021 to April 2022. The HY-2B penetration coefficient is calculated for the first time to correct the freeboard in areas with sea ice concentration greater than 90%. The estimation accuracy is improved by enhancing the data on sea ice density, seawater density, snow depth, and snow density. The research analyzed the effects of snow depth and penetration coefficient on sea ice thickness results. The results of sea ice type classification were compared with OSI-SAF ice products, and the sea ice thickness estimation results were compared with four satellite ice thickness products (CryoSat-2 and SMOS (CS-SMOS), Centre for Polar Observation and Modelling Data (CPOM), CryoSat-2 (CS-2), and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)) as well as two validation ice thickness data sets (Operation IceBridge (OIB) and ICEBird). The accuracy of sea ice classification exceeds 92%, which is in good agreement with ice type product data. The RMSD of sea ice thickness estimation is 0.56 m for CS-SMOS, 0.68 m for CPOM, 0.47 m for CS-2, 0.69 m for PIOMAS, and 0.79 m for validation data. Full article

In this study, wind speed sensitivity and calibration bias were first determined for Surface Water and Ocean Topography (SWOT) satellite Ka-band Radar Interferometer (KaRIn) Normalized Radar Backscatter Cross Section (NRCS) data at VV and HH polarizations. Here, the calibration bias was estimated by comparing the KaRIn NRCS with collocated simulations from a model developed using Global Precipitation Measurement (GPM) satellite Dual-frequency Precipitation Radar (DPR) data. To recalibrate the bias, the correlation coefficient between the KaRIn data and the simulations was estimated, and the data with the corresponding top 10% correlation coefficients were used to estimate the recalibration coefficients. After recalibration, a Ka-band NRCS model was developed from the KaRIn data to retrieve ocean surface wind speeds. Finally, wind speed retrievals were evaluated using the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis winds, Haiyang-2C scatterometer (HY2C-SCAT) winds and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean (TAO) buoy winds. Evaluation results show that the Root Mean Square Error (RMSE) at both polarizations is less than 1.52 m/s, 1.34 m/s and 1.57 m/s, respectively, when compared to ECMWF, HY2C-SCAT and buoy collocated winds. Moreover, both the bias and RMSE were constant with the incidence angles and polarizations. This indicates that the winds from the SWOT KaRIn data are capable of correcting the sea state bias for sea surface height products. Full article

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

The Haiyang-2D (HY2D) satellite is the fourth ocean dynamics environment monitoring satellite launched by China. The satellite operates on a re-entry frozen orbit, which necessitates orbital maneuvers to maintain its designated path once the satellite’s sub-satellite point deviates beyond a certain threshold. However, the execution of orbit maneuvers presents a significant challenge to the field of Precise Orbit Determination (POD). The thesis selects the on-board GPS data of HY2D satellite in December 2023 and five maneuvering days of that year. Employing a multifaceted approach that includes the assessment of observational data quality, orbit overlap, external orbit validation, and SLR (Satellite Laser Ranging) verification, the research delves into precise orbit determination during both maneuver and non-maneuver periods. The results indicate that: (1) The average number of satellites tracked by the receiver is 6.4; (2) During the non-maneuver periods, the average RMS (Root Mean Square) value of the radial difference in the 6-h overlapping arc segment is 0.66 cm, and the three-dimensional position difference is about 1.16 cm; (3) When compared with the precision science orbits (PSO) provided by CNES (Centre National d’Études Spatiales), the average values of the RMS values of the differences in the radial (R), transverse (T), and normal (N) directions during the non-maneuver and maneuver periods are respectively 1.32 cm, 2.31 cm, 1.92 cm and 3.04 cm, 8.78 cm, 2.72 cm. (4) The SLR verification of the orbit revealed a residual RMS of 2.24 cm. This suggests that by incorporating the modeling of maneuver forces during the maneuver periods, the impact of orbital maneuvers on orbit determination can be mitigated. Full article

Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons. Full article

Climate data derived from long-term, multisource altimeter significant wave height (SWH) measurements are more valuable than those obtained from a single altimeter source. Such data facilitate exploration of long-term air–sea momentum transfer and more comprehensive investigation of weather system dynamics processes over the ocean. Despite the deployment of the first satellite in the Chinese Haiyang-2 (HY-2) series more than 12 years ago, validation and integration of SWH data from China’s offshore waters, derived using Chinese altimeters, have been limited. This study constructed a high-resolution, long-term, multisource gridded SWH climate dataset using along-track data from the HY-2 series, CFOSAT, Jason-2, Jason-3, and Cryosat-2 altimeters. Validation against observations from 31 buoys covering China’s offshore waters indicated that the SWH variances from HY-2A, HY-2B, HY-2C, CFOSAT, and Jason-3 altimeters correlated well with observations, with a temporal correlation coefficient of approximately 0.95 (except HY-2A, correlation: 0.89). These SWH measurements generally showed a robust linear relationship with the buoy data. Additionally, cross-calibration between Jason-3 and the HY-2A, HY-2B, HY-2C, and CFOSAT altimeters also demonstrated a typically linear relationship for SWH > 6.0 m. Using this relationship, the SWH data were linearly corrected and integrated into a 10 d mean, long-term, multisource altimeter gridded SWH dataset. Compared with in situ observations, the merged 10 d mean SWHs are more accurate and closely match the observations, with temporal correlation coefficients improving from 0.87 to 0.90 and bias decreasing from 0.28 to 0.03 m. The merged gridded SWHs effectively represent the local spatial distribution of SWH. This study revealed the importance of observational data in the process of merging and recalibrating long-term multisource altimeter SWH datasets, particularly before their application in specific ocean regions. Full article

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. Full article

Haiyang-2A (HY-2A) is the first marine dynamic environment satellite established by China, which has made significant contributions to the marine scientific research field. It carries the satellite-based GPS receiver named HY2, which was independently developed by China. It is an experimental satellite-borne GPS receiver for low earth orbit satellites, and during its operational period in orbit, the satellite-borne GPS data are not made accessible to the public. Therefore, this paper assesses the quality of HY-2A satellite-borne GPS data based on indicators such as satellite visibility, multipath effect, and ionospheric delay. The results indicate that the data acquired by the HY2 receiver are of high quality. The precise orbit determination (POD) uses the reduced-dynamic (RD) method. The adjustment effects of the pseudo-stochastic pulse time interval and a priori sigma on POD are analyzed, and the antenna phase center variation (PCV) is estimated using the direct method and residual method. Furthermore, this paper investigates the impact of PCV models with different resolutions (10° × 10° and 5° × 5°) on satellite orbit determination. To evaluate the orbit precision, three methods are used for validation, including carrier phase residual analysis, external precise science orbit (PSO) validation, and SLR three-dimensional (3D) validation, respectively. The results indicate that the highest orbit precision is achieved when the pseudo-stochastic pulse time interval is configured to 15 min, with the a priori sigma of 1 × 10⁻⁸ m/s². The orbit carrier phase residuals reach the millimeter level. The 10° × 10°PCV model was estimated using the direct method and residual method, respectively; the root mean square of the external orbit validation for both methods show a millimeter-level improvement. The results obtained from the direct method and residual method are comparable. The resolution is increased from 10° to 5°, and the improvement in orbital precision is relatively small. The results obtained from the SLR 3D validation are consistent with those from the external PSO validation. The experimental results contribute valuable information for the POD of the HY2 series satellites. Full article

Haiyang-1D (HY-1D) is the second operational satellite in China’s Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved. Full article

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms. Full article

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. Full article

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. Full article