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In this paper, a fusion model based on convolution and self-attention with multi-subimage input model (CNN-SA-MS) is proposed to estimate significant wave height (SWH) from shipborne X-band radar images. The model takes multiple radar subimages as input simultaneously, which not only improves the accuracy of SWH inversion by including more information, but also avoids the restriction of selecting a single subimage in the upwind direction and dependence on external devices for wind data provision. Based on the characteristics of radar images and computational efficiency considerations, this paper selects three radar subimages as the input for the model. The comparison data from buoys and ECMWF are used for training and testing. After averaging the results of 64 radar images, the root mean square error (RMSE) and correlation coefficient (CC) of the CNN-SA-MS model are 0.197 m and 0.903, respectively. The results show that the CNN-SA-MS model improves the accuracy and stability of SWH estimation compared to single-subimage CNN regression model. For the two time periods with significant discrepancies between radar data and ECMWF predictions, we introduce satellite altimeter information as a source of reference for evaluation. The resulting analysis indicates that the significant wave height estimates generated by CNN-SA-MS model are more reliable. [ABSTRACT FROM AUTHOR]

This study addresses the global shift towards renewable energy due to the increasing demand driven by socioeconomic development. With finite fossil energy sources, there's a growing interest in oceanic renewable energy, estimated at 76 million MW. The Amazon, with its significant potential, particularly from predictable tidal currents in estuaries, stands out. The Inner Continental Shelf of Amapá, influenced by factors like water discharge (5,7 × 1012 m3.year− 1) and trade winds (speed: 5–10 m.s− 1) as well as Meso and Macrotidal, is a notable region. The paper focuses on hydrodynamic simulations considering different tidal scenarios and aims to assess the energy potential of tidal currents. Using the SisBaHiA two-dimensional hydrodynamic model, the study evaluates power density in key areas. The spring-neap cycle simulations identify promising zones in the Varador channel—upper section (tidal current velocity of 1,53 m.s− 1– 1.835 W.m− 2) and lower section (tidal current velocity of 1,12 m.s− 1 – 720,03 W.m-2). Notably, the upper Varador channel near the Inferno ¨igarapé¨ (Maraca Island) exhibits greater energy density, highlighting its potential in both spring and neap cycles. This research contributes valuable insights into the renewable energy potential of tidal currents in the Amapá region, supporting sustainable energy development. [ABSTRACT FROM AUTHOR]

The paper presents the reconstruction of sea levels in the North Sea and Baltic Sea using Kalman filter approach. Based on the statistical characteristics of one year of daily maps of sea level from the Geesthacht COAstal model SysTem (GCOAST) and daily data at tide gauges along the coastline of two basins, the method can reconstruct effectively and accurately the multidecadal sea level anomalies. The high accuracy reconstruction data were then used to investigate the interannual variability in both basins and to estimate the difference between outflows and inflows (net flux) through the Danish Straits. The highest mean, standard deviation, and extreme values of sea level anomalies appear in winter and are well reproduced in different regions, such as the German Bight, the Southern North Sea, the Bothnian Bay, the Gulfs of Finland and Riga. The sea level variability is highly correlated with the mean sea level pressure and the zonal wind, particularly in the German Bight and in the winter months. The contributions of river runoff and net precipitation on the net flux are significant in the spring. The local wind has a greater influence on the net flux than the remote drivers. [ABSTRACT FROM AUTHOR]

The 12th International Workshop on Modeling the Ocean (IWMO 2022) was held in Ann Arbor, Michigan, USA from June 28 to July 1, 2022. The workshop, organized by the Cooperative Institute for Great Lakes Research (CIGLR) and NOAA Great Lakes Environmental Research Laboratory (GLERL), aimed to bring together scientists from different countries to encourage international research collaborations on ocean modeling, prediction, and data analysis. The meeting featured presentations on various topics related to ocean modeling and prediction, including waves, currents, ice, marine events, and extreme weather. Due to the ongoing COVID-19 pandemic, the workshop employed a hybrid format with both in-person and online presentations. The event included 66 scientists and graduate students from 12 different countries. A collection of 11 peer-reviewed papers from the workshop participants was published in the journal Ocean Dynamics, covering topics such as hydrodynamics, sea-level rise, extreme sea levels, wave climate trends, marine debris forecasting, flood forecasting, and ice-ocean modeling. The workshop was considered a success, and efforts in recent years have focused on the development of modeling the Great Lakes system. [Extracted from the article]

This document is a correction notice for an article titled "Relative dispersion and relative diffusivities in an ocean-wave coupled model of the North Sea" published in Ocean Dynamics. The correction states that the author forgot to include some projects in the acknowledgement section of the paper. The projects mentioned include the EU Green Deal project REST-COAST, the project Macroplastics Pollution in the Southern North Sea-Sources, Pathways and Abatement Strategies, the project CRANMANII, the EU project DOORS, the Gute Küste Niedersachsen project, the Sailing Intelligent Micro Drifter Swarms project, and the Volkswagen Foundation. The original article has been corrected. [Extracted from the article]

The original article can be found online at https://doi.org/10.1007/s10236-023-01568-6.Correction to: Ocean Dynamicshttps://doi.org/10.1007/s10236-023-01568-6The Images in figures 3 to 12 in the published paper are out of position. The image corresponding to figure 3 is supposed to be a summary of the Lorenz Energy Cycle, but it was omitted.The original article has been corrected.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By John Ssebandeke; Jin-Song von Storch and Nils BrüggemannReported by Author; Author; Author [Extracted from the article]