Na Liu

To address challenges in large-scale deployments and real-time wave observations using GNSS buoys, we assessed the reliability and precision of the single-frequency L1 Time-Differenced Carrier Phases (TDCP) method for determining significant wave heights (SWH) and average wave periods. Utilizing simulated dynamic velocity measurements derived from International GNSS Service static observations, our study demonstrates that the L1 TDCP method exhibits superior performance compared to dual-frequency TDCP, Post-Processed Kinematic (PPK), and Doppler algorithms. Notably, L1 TDCP achieves approximately five times greater precision than the Doppler method in vertical direction assessments. In the offshore waters of Qingdao, China, we deployed two GNSS buoys for wave observation experiments, complemented by a nearby accelerometer-equipped wave buoy for external validation. By integrating the velocity measurements obtained from L1 TDCP, we derived the sea surface displacement, which, after applying a high-pass filter, isolated the wave components. The experimental results confirmed a strong correlation between L1 TDCP and PPK methods in capturing SWH and wave periods. The study found a negligible difference between L1 TDCP and PPK in SWH (0 ± 7.1mm, 99% correlation) and in average wave period (0 ± 0.13s, 97% correlation). The wave observations from two independent GNSS buoys at the same location also exhibited remarkable consistency, with a difference in SWH of 0.0 ± 2.5cm and in average wave period of 0.0 ± 0.1s. Compared to the independent wave buoy, the L1 TDCP method demonstrated a difference in SWH of -1.9 ± 5.8cm with a 93% correlation, and in average period of 0 ± 0.1s with a 94% correlation. Further, our analysis into GNSS data sampling frequencies highlighted the efficacy of 1Hz GNSS data in wave inversion, though certain spectral limitations were noted. Implementing the L1 TDCP method at 1Hz offers a substantial reduction in associated hardware costs, storage requirements, and computational demands. Notably, in contrast to PPK, it negates the need for delayed precise ephemeris, facilitating real-time computations. This study highlights the potential of L1 TDCP for broader, real-time GNSS buoy wave observations, harmonizing with the United Nations Decade of Ocean Science’s aspirations for an augmented Global Ocean Observing System.

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions.

In the Bering Sea slope, ocean eddies are essential physical processes that carry nutrients to the shelf. The development of the satellite altimeter has facilitated the observation of oceanic eddies. Attention networks are used as the core algorithm for eddy detection to suppress feature responses in irrelevant non-eddy areas, which can address the issue of sample imbalance in high-latitude ocean eddies. Furthermore, data from both the sea surface height (SSH) and geostrophic velocity were employed as model inputs to integrate more eddy-related properties. The results of ocean eddy detection using this method and the dataset allowed more eddies to be detected than with traditional vector geometry-based methods and only SSH-based models. This study also incorporated the results of multiple deep learning models to increase both the overall and single-day eddy detection efficiency. As a result, the algorithms in this paper show that attention networks and geostrophic velocity data are both appropriate for high-latitude ocean eddy identification. This makes a contribution to the further application of deep learning methods to satellite altimetry data.

Arctic sea ice is a key factor in high–latitude air–sea–ocean interactions. In recent decades, its extent has been decreasing in all seasons with large interannual variability, especially for the Northwind Ridge. After removing the trend in the changes during July 1979 to 2020, 2019 had an abnormally low value, while the following year, 2020, had an abnormally high value. The underlying processes driving this variability in July near the southern Northwind Ridge, which is one of the areas with the most drastic changes in Arctic, are not well understood. There, we demonstrated that the shortwave radiation anomaly in July is the direct reason for the sea ice anomaly in July 2019 and July 2020. Importantly, the total energy surplus in the spring of 2019 (enough to melt ∼18 cm of sea ice) and 2020 (potentially melting ∼11 cm of sea ice) indirectly influenced the sea ice. The abnormal change in moisture and its convergence mainly caused by atmospheric circulation were the main reasons for the longwave radiation and latent flux anomalies. Cloud water mainly affected shortwave radiation, including the positive net shortwave radiation anomaly in May 2019.

Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry’s overall development and research front.

Over the past few decades, the areal extent of the Arctic sea ice cover has decreased. During the winter of 2017, negative sea ice concentration anomalies occurred mainly in the Chukchi Sea and adjacent seas. The properties of Pacific water through the Bering Strait have changed in recent years. To highlight the role of the Pacific inflow during the 2017 Arctic sea ice retreat, we used mooring measurements and conductivity–temperature–depth (CTD) data to quantify the effect of inflow on sea ice in the Chukchi shelf. In September 2017, the temperature of the Pacific inflow was relatively high compared with the multi-year average, especially in the shelf north of 69°N where the temperature anomaly was generally greater than 1°C. The average heat content of each CTD station in September 2017 ranged from 0.77 to 1.58 GJ m^–2, where each station was 0.25 GJ m^–2 higher than the multi-year average. In the central shelf of the Chukchi Sea, the temperature of the 25–40 m layer increased after late May, and decreased after mid-September. The Pacific inflow could have provided a large amount of heat to the Chukchi shelf, the accumulated convective heat transported to the surface from September to October was approximately 1.68 × 10¹⁸ J and it impacted the sea ice growth conditions.

Taken the deep‐sea gravity‐piston corer as the prototype, a model of the corer penetrating was built based on energy conservation and by analysing the coring process. The effects of influencing parameters of the deep‐sea gravity‐piston corer on the penetration process and coring length were analysed. In addition, a comparison between the calculated values and the real examples was performed. The results show that corer weight, sediment properties and penetration velocity affect the coring performance. The penetration depth significantly increases with an increase in corer weight, and the corer weight plays an important role in the process of the corer penetrating. Moreover, the penetration depth decreases with the friction coefficient of increasing sediment, while it gradually increases with an increase in penetration velocity. It is also revealed that the penetration depth decreases approximately linearly as the core barrel diameter. Consequently, these will provide the basic data for the design and optimisation of deep‐water gravity‐piston corer.