In the grid processing of the deflection of the vertical (DOV) for marine gravity recovery, the traditional least squares collocation (LSC) gridding method only considers the accuracy difference between different satellite radar altimeters, but ignores the accuracy difference between different observation points of the same radar altimeter. In order to solve this problem, a new LSC gridding method weighted by the accuracy of observation points is proposed in this letter. The new method sets the quality parameters of each observed value according to the dispersion of the data and gives corresponding weights to each residual along-track DOV for LSC gridding based on the error propagation law. Then, based on the $1^\prime \times 1^\prime $ gridded DOV obtained by the traditional method and the new method, the inverse Vening Meinesz formula with 1-D fast Fourier transform (FFT) and the remove and restore method were used to recover the $1^\prime \times 1^\prime $ gridded gravity field in the South China Sea. The validation of ship-measured gravity data shows that the new DOV gridding method can effectively improve the accuracy of the marine gravity field derived from HY-2A and CryoSat-2 data. The new method is also applicable to other similar satellite altimeter data, and will bring significant improvements to the recovery of higher-precision ocean gravity fields.

Among the marine gravity field models derived from satellite altimetry, the Scripps Institution of Oceanography (SIO) series and Denmark Technical University (DTU) series models are the most representative and are often used to integrate global gravity field models, which were inverted by the deflection of vertical method and sea surface height method, respectively. The fusion method based on the offshore distance used in the EGM2008 model is just model stitching, which cannot realize the true fusion of the two types of marine gravity field models. In the paper, a new fusion method based on water depth segmentation is proposed, which established the Precision–Depth relationship of each model in each water depth segment in the investigated area, then constructed the FUSION model by weighted fusion based on the precision predicted from the Precision–Depth relationship at each grid in the whole region. The application in the South China Sea shows that the FUSION model built by the new fusion method has better accuracy than SIO28 and DTU17, especially in shallow water and offshore areas. Within 20 km offshore, the RMS of the FUSION model is 5.10 mGal, which is 8% and 4% better than original models, respectively. Within 100 m of shallow water, the accuracy of the FUSION model is 4.01 mGal, which is 14% and 12% higher than the original models, respectively. A further analysis shows that the fusion model is in better agreement with the seabed topography than original models. The new fusion method can blend the effective information of original models to provide a higher-precision marine gravity field.

In early 2025, the Jason-3 satellite’s orbit shifted from an “interleaved” to a tandem configuration with Sentinel-6A, and its Geophysical Data Records (GDR) were upgraded from Version F to G. This study evaluated GDR-G via eight processing approaches, using Jason-3’s last six GDR-F cycles (#394–#399) and first six GDR-G cycles (#501–#506), integrating histogram/geographical distribution analyses of Sea Surface Height Anomaly (SSHA), Significant Wave Height (SWH), Wind Speed (WS), and multi-method validation (e.g., self-cross-calibration). Key findings include the following: GDR-G had significantly lower SSHA noise than GDR-F, with up to ~4 cm SSHA bias from different retrackers/corrections; Adaptive retracker + 3D Sea State Bias (SSB) correction achieved optimal accuracy. Adaptive retracker’s SWH/WS anomalies linked to invalid MLE4 results and non-Brownian waveforms (coastal/sea ice). A detrending method was proposed, and the 41-point Lanczos window was optimal for smoothing. The results from the “detrending method” were consistent with the results based on the SSHA spectrum and classic self-cross-calibration methods. A ~5 mm drop was observed in Jason-3 GDR-G MLE4 baseline SSHA, probably caused by GDR upgrade or geographic sampling mismatch, while Sentinel-6A’s GDR-G upgrade might induce ~1 cm jump. The jumps along with GDR version upgrade highlighted the value of timely in situ absolute calibration.

海洋重力场是地球系统的重要物理场, 船测重力与卫星测高重力是其高分辨率建模的主要数据源, 然而, 船测重力数据的空间覆盖在全球范围内稀疏且不均匀, 单一测高重力模型在特定区域往往存在精度局限或覆盖不全的问题. 本文利用西太平洋海域(120°E－170°E, 0°－50°N)JAMSTEC高密度船测重力数据, 基于国际上两大测高重力场系列模型的新版本DTU21和SIOv32.1, 建立重力模型误差与SRTM15v2.4水深的对应关系, 通过对两个模型进行加权融合, 并对极区进行特殊处理, 生成全球1'×1'网格海洋重力异常模型FUSION_V1.0. 该模型包含经度、纬度和重力异常变量, 覆盖全球海洋(90°S－90°N, 180°W－180°E), 采用最为简单通用的地理坐标系统(即等经纬度投影), 以NetCDF格式存储, 支持主流科研软件读取. 本数据集可为海底地形反演、海洋地球物理研究和空间科学应用提供高质量基础数据.

In early 2025, the Jason-3 satellite orbit was maneuvered from the "interleaved" orbit to a tandem configuration with the Sentinel-6A satellite, concurrent with the upgrade of its Geophysical Data Records (GDR) from Version F to G. To date, six complete cycles of GDR-G products have been released, featuring notable improvements such as the official replacement of the MLE3 retracker with the Adaptive retracker for Sea Surface Height Anomaly (SSHA) calculations. This study aims to evaluate GDR-G performance by analyzing SSHA under eight error correction strategies, using the last six GDR-F cycles and first six GDR-G cycles. Key findings include: significantly reduced SSHA noise in GDR-G compared to GDR-F; relative biases of up to ~4 cm in sea surface height when using different retracking algorithms or correction strategies; and optimal accuracy achieved with the Adaptive retracker combined with 3D sea state bias (SSB) correction. Comparative analysis of Significant Wave Height (SWH), backscattering coefficient (Sigma-0), and wind speed (WS) between MLE4 and Adaptive retrackers revealed anomalies in Adaptive results: clustered SWH outliers near 0 m and 4 m, and WS outliers slightly above 28 m/s. These anomalies, linked to invalid MLE4 retracking results and non-Brownian echo shapes (coastal/inland waters, sea ice), suggest that refining the Adaptive retracker or developing new algorithms could further enhance Jason-3’s observational performance.