Quality control (QC) is an essential procedure in scatterometer wind retrieval, which is used to distinguish good-quality data from poor-quality wind vector cells (WVCs) for the sake of wind applications. The current wind processor of the China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) adopts a maximum likelihood estimator (MLE)-based QC method to filter WVCs affected by geophysical noise, such as rainfall and wind variability. As the first Ku-band rotating fan-beam scatterometer, CSCAT can acquire up to 16 observations over a single WVC, giving abundant information with diverse incidence/azimuth angles, as such its MLE statistical characteristics may be different from the previous scatterometers. In this study, several QC indicators, including MLE, its spatially averaged value (MLE_m), and the singularity exponents (SE), are analyzed using the collocated Global Precipitation Mission rainfall data as well as buoy data, to compare their sensitivity to rainfall and wind quality. The results show that wind error characteristics of CSCAT under different QC methods are similar to those of other Ku-band scatterometers, i.e., SE is more suitable than other parameters for the wind QC at outer-swath and nadir regions, while MLE_m is the best QC indicator for the sweet region WVCs. Specifically, SE is much more favorable than others at high wind speeds. By combining different indicators, an improved QC method is developed for CSCAT. The validation with the collocated buoy data shows that it accepts more WVCs, and in turn, improves the quality control of CSCAT wind data.

The Stokes production coefficient (E₆) constitutes a critical parameter within the Mellor-Yamada type (MY-type) Langmuir turbulence (LT) parameterization schemes, significantly affecting the simulation of turbulent kinetic energy, turbulent length scale, and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean. However, the accurate determination of its value remains a pressing scientific challenge. This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E₆. Through the integration of the information of the turbulent length scale equation into a physical-informed neural network (PINN), we achieved an accurate and physically meaningful inference of E₆. Multiple cases were examined to assess the feasibility of PINN in this task, revealing that under optimal settings, the average mean squared error of the E₆ inference was only 0.01, attesting to the effectiveness of PINN. The optimal hyperparameter combination was identified using the Tanh activation function, along with a spatiotemporal sampling interval of 1 s and 0.1 m. This resulted in a substantial reduction in the average bias of the E₆ inference, ranging from O(10¹) to O(10²) times compared with other combinations. This study underscores the potential application of PINN in intricate marine environments, offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

Surface Water and Ocean Topography (SWOT) is a next-generation radar altimeter that offers high resolution, wide swath, imaging capabilities. It has provided free public data worldwide since December 2023. This paper aims to preliminarily analyze the detection capabilities of the Ka-band radar interferometer (KaRIn) and Nadir altimeter (NALT), which are carried out by SWOT for internal solitary waves (ISWs), and to gather other remote sensing images to validate SWOT observations. KaRIn effectively detects ISW surface features and generates surface height variation maps reflecting the modulations induced by ISWs. However, its swath width does not completely cover the entire wave packet, and the resolution of L2/L3 level products (about 2 km) cannot be used to identify ISWs with smaller wavelengths. Additionally, significant wave height (SWH) images exhibit blocky structures that are not suitable for ISW studies; sea surface height anomaly (SSHA) images display systematic left-right banding. We optimize this imbalance using detrending methods; however, more precise treatment should commence with L1-level data. Quantitative analysis based on L3-level SSHA data indicates that the average SSHA variation induced by ISWs ranges from 10 cm to 20 cm. NALTs disturbed by ISWs record unusually elevated SWH and SSHA values, rendering the data unsuitable for analysis and necessitating targeted corrections in future retracking algorithms. For the normalized radar cross section, Ku-band and four-parameter maximum likelihood estimation retracking demonstrated greater sensitivity to minor changes in the sea surface, making them more suitable for ISW detection. In conclusion, SWOT demonstrates outstanding capabilities in ISW detection, significantly advancing research on the modulation of the sea surface by ISWs and remote sensing imaging mechanisms.

The mesoscale eddy (ME) has a significant influence on the convergence effect in deep-sea acoustic propagation. This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone (CZ) characteristics. Based on the Gaussian vortex model, we construct various sound propagation scenarios under different eddy conditions, and carry out sound propagation experiments to obtain simulation samples. With a large number of samples, we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters. The sensitivity of eddy indicators to the CZ is quantitatively analyzed. Then, we adopt the machine learning (ML) algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters. Through the research, we can express the influence of ME on the CZ quantitatively, and achieve the rapid prediction of CZ parameters in ocean eddies. The prediction accuracy (R) of the CZ distance (mean R: 0.9815) is obviously better than that of the CZ width (mean R: 0.8728). Among the three ML algorithms, Gradient Boosting Decision Tree has the best prediction ability (root mean square error (RMSE): 0.136), followed by Random Forest (RMSE: 0.441) and Extreme Learning Machine (RMSE: 0.518).

Sea ice surface roughness (SIR) affects the energy transfer between the atmosphere and the ocean, and it is also an important indicator for sea ice characteristics. To obtain a small-scale SIR with high spatial resolution, a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar (SAR) images, utilizing an ensemble learning method. Firstly, the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice, including the scale and direction of ice patterns. Secondly, a model is developed using the Adaboost Regression model to establish a relationship among SIR, radar backscatter and the spatial information of sea ice. The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper (ATM) in the summer Beaufort Sea. The determination of coefficient, mean absolute error, root-mean-square error and mean absolute percentage error of the testing data are 0.91, 1.71 cm, 2.82 cm, and 36.37%, respectively, which are reasonable. Moreover, K-fold cross-validation and learning curves are analyzed, which also demonstrate the method’s applicability in retrieving SIR from SAR images.

A high-resolution customized numerical model is used to analyze the water transport in the three major water passages between the Andaman Sea (AS) and the Bay of Bengal, i.e., the Preparis Channel (PC), the Ten Degree Channel (TDC), and the Great Channel (GC), based on the daily averaged simulation results ranging from 2010 to 2019. Spectral analysis and Empirical Orthogonal Function (EOF) methods are employed to investigate the spatiotemporal variability of the water exchange and controlling mechanisms. The results of model simulation indicate that the net average transports of the PC and GC, as well as their linear trend, are opposite to that of the TDC. This indicates that the PC and the GC are the main inflow channels of the AS, while the TDC is the main outflow channel of the AS. The transport variability is most pronounced at surface levels and between 100 m and 200 m depth, likely affected by monsoons and circulation. A 182.4-d semiannual variability is consistently seen in all three channels, which is also evident in their second principal components. Based on sea level anomalies and EOF analysis results, this is primarily due to equatorial winds during the monsoon transition period, causing eastward movement of Kelvin waves along the AS coast, thereby affecting the spatiotemporal characteristics of the flow in the AS. The first EOF of the PC flow field section shows a split at 100 m deep, likely due to topography. The first EOF of the TDC flow field section is steady but has potent seasonal oscillations in its time series. Meanwhile, the first EOF of the GC flow field section indicates a stable surface inflow, probably influenced by the equatorial Indian Ocean’s eastward current.

Estimated ocean subsurface fields derived from satellite observations provide potential data sources for operational marine environmental monitoring and prediction systems. This study employs a statistic regression reconstruction method, in combination with domestic autonomous sea surface height and sea surface temperature observations from the Haiyang-2 (HY-2) satellite fusion data, to establish an operational quasi-real-time three-dimensional (3D) temperature and salinity products over the Maritime Silk Road. These products feature a daily temporal resolution and a spatial resolution of 0.25° × 0.25° and exhibit stability and continuity. We have demonstrated the accuracy of the reconstructed thermohaline fields in capturing the 3D thermohaline variations through comprehensive statistical evaluations, after comparing them against Argo observations and ocean analysis data from 2022. The results illustrate that the reconstructed fields effectively represent seasonal variations in oceanic subsurface structures, along with structural changes resulting from mesoscale processes, and the upper ocean’s responses to tropical cyclones. Furthermore, the incorporation of HY-2 satellite observations notably enhances the accuracy of temperature and salinity reconstructions in the Northwest Pacific Ocean and marginally improves salinity reconstruction accuracy in the North Indian Ocean when compared to the World Ocean Atlas 2018 monthly climatology thermohaline fields. As a result, the reconstructed product holds promise for providing quasi-real-time 3D temperature and salinity field information to facilitate fast decision-making during emergencies, and also offers foundational thermohaline fields for operational ocean reanalysis and forecasting systems. These contributions enhance the safety and stability of ocean subsurface activities and navigation.

Multiple borehole samples are collected from the Baiyun Depression in deep-water area of the northern South China Sea (SCS) in an effort to reconstruct transgression processes during the Paleogene based on palynalgal analysis. This study indicates that the Baiyun Depression generated a large group of palynopore assemblages and fluvial/lacustrine-related algae during the early and middle Eocene when the Wenchang Formation was deposited. The entire depression was dominated by fluvial and lacustrine facies before transgression. Its eastern and southeastern sags transitioned to shallow marine environment by generating a large abundance of marine dinoflagellates during the Enping deposition of the late Eocene. Meanwhile, the southern uplift zone simply yielded fluvial/lacustrine-related palynopores and algae, and was dominated by the fluvial and lacustrine environment during the early stage of the Enping Formation, prior to shifting into transitional setting in the later period. Northwestern sags remained extensive fluvial and delta facies without existence of marine dinoflagellates. It was until the depositional stage of the Zhuhai Formation (Oligocene) that the overall depression was strongly impacted from transgression process. Both eastern and southeastern sags were mainly under deep marine setting on a continental slope while northwestern and southern areas developed transitional facies. Although distribution and accumulation patterns varied greatly among sub-sags, the overall Baiyun Depression was characterized by widespread development of marine dinoflagellates. It should be noted that the northwestern sag also partly generated large-scale river delta deposits. Due to the eustatic rise and change of SCS spreading axis, the overall Baiyun Depression was mostly influenced by the deep marine environment on a continental slope during the early Miocene. Both northwestern sag and southern uplift zone were found plentiful marine dinoflagellates. In summary, transgression initiated from the eastern and southeastern Baiyun Depression before subsequently progressing into the farther west. Evolution of transgression process is also greatly consistent with the gradual westward expansion of the SCS.

Significant advancements have been made in the study of Mesozoic granite buried hills in the Songnan Low Uplift (SNLU) of the Qiongdongnan Basin. These findings indicate that the bedrock buried hills in this basin hold great potential for exploration. Borehole samples taken from the granite buried hills in the SNLU were analyzed using apatite fission track (AFT) and zircon (U-Th)/He data to unravel the thermal history of the basement rock. This information is crucial for understanding the processes of exhumation and alteration that occurred after its formation. Thermal modeling of a sample from the western bulge of the SNLU revealed a prolonged cooling event from the late Mesozoic to the Oligocene period (~80−23.8 Ma), followed by a heating stage from the Miocene epoch until the present (~23.8 Ma to present). In contrast, the sample from the eastern bulge experienced a more complex thermal history. It underwent two cooling stages during the late Mesozoic to late Eocene period (~80−36.4 Ma) and the late Oligocene period (~30−23.8 Ma), interspersed with two heating phases during the late Eocene to early Oligocene period (~36.4−30 Ma) and the Miocene epoch to recent times (~23.8−0 Ma), respectively. The differences in exhumation histories between the western and eastern bulges during the late Eocene to Oligocene period in the SNLU can likely be attributed to differences in fault activity. Unlike typical passive continental margin basins, the SNLU has experienced accelerated subsidence after the rifting phase, which began around 5.2 Ma ago. The possible mechanism for this abnormal post-rifting subsidence may be the decay or movement of the deep thermal source and the rapid cooling of the asthenosphere. Long-term and multi-episodic cooling and exhumation processes play a key role in the alteration of bedrock and contribute to the formation of reservoirs. On the other hand, rapid post-rifting subsidence (sedimentation) promotes the formation of cap rocks.

The Zambezi Delta basin is a passive marginal basin located on the East African coast that has good oil and gas exploration potential. Due to the special geological evolutionary background of the Beira High in the Zambezi Delta basin, it has a low gravity anomaly, and the existing seismic survey lines do not cover the whole basin; therefore, it is difficult to interpret the structural characteristics of the whole basin based solely on gravity or seismic data. Based on satellite altimetry gravity anomaly data, this study infers the distribution characteristics of faults in the Zambezi Delta basin by using the normalized vertical derivative of the total horizontal derivative (NVDR-THDR) technique. Then, constrained by seismic data, the gravity anomaly at the Moho interface is extracted by using the fast forward method of the double-interface model of the gravity anomaly, and this anomaly is then removed from the Bouguer gravity anomaly to obtain the sedimentary layer gravity anomaly. The thickness of the sedimentary strata is obtained by inversing the sedimentary basement depth of the whole basin. Then, uplifts and depressions are divided based on a sedimentary layer thickness of 3 km. This research demonstrates that the Zambezi Delta basin mainly features nearly SN-trending and NE-trending faults and that these faults exhibit east‒west partitioning. The nearly SN-trending strike-slip faults controlled the sedimentary development of the basin, and the NE-trending tensile faults may have acted as migration channels for oil, gas and magma. The “overcompensation” effect of the Moho interface gravity anomaly on the gravity anomaly of the sedimentary layer is caused by the depression of the Moho interface beneath the Beira High, which results in a low gravity anomaly value for the Beira High. The pattern of uplifts and depressions trends NE and has the structural characteristics of east‒west blocks.