Monitoring sea state is essential for navigation safety, vessel operability, and offshore/coastal engineering, as it supports the estimation of key parameters such as sea-surface geometry and significant wave height. Traditionally, sea-state information is obtained from in situ instruments (e.g., wave buoys and ship motion sensors) and remote observations (e.g., radar-based systems and satellite products). While these approaches are mature, they may be limited by spatial/temporal coverage, deployment, and maintenance constraints. In contrast to well-established fixed-station solutions, shipborne observations are emerging, coping with non-stationary viewing geometry and vessel dynamics. This motivates system integration, such as stereophotogrammetry with Global Navigation Satellite System (GNSS) observations to provide an alternative route to measure wave metrics from the reconstructed sea surface geometry. The present work describes a shipborne sensing system built around a time-disciplined camera and GNSS synchronization to enable stereo-vision processing and subsequent generation of a 3D point cloud of the sea surface. The whole project approach is technically challenging in realistic marine conditions (e.g., changing illumination, specular reflections, low texture, and intermittent occlusions). The acquisition chain utilizes a GNSS Pulse-Per-Second (PPS) signal to trigger two industrial RGB cameras synchronized via hardware, with deterministic triggering and logging managed by a Raspberry Pi 4. PPS-based triggering provides stable frame time-stamping, enabling coherent fusion with and motion (ship attitude and trajectory) information provided by GNSS. Ongoing tests focus on end-to-end robustness (timing stability, synchronization, and motion sensitivity) and on comparison against independent references when available (e.g., onboard motion sensors and nearby in situ records). The proposed configuration provides a useful instrument for sea-state monitoring, in a scalable and low-cost method.

How to cite: Mammadova, S., Ferrando, I., and Sguerso, D.: Integrated stereo vision and GNSS approach for sea-state monitoring on a moving vessel, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2994, https://doi.org/10.5194/egusphere-egu26-2994, 2026.

How to cite: Benchaabane, A., De Carlo, M., Husson, R., and Peureux, C.: Bridging Satellite Observations: A Comprehensive Intercomparison of Wave Spectra, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20698, https://doi.org/10.5194/egusphere-egu26-20698, 2026.

The SWOT satellite was launched in December 2022 and employs a novel SAR-in technology to achieve high-precision measurements over inland waters and the ocean. For ocean observations, SWOT provides low-rate data products, referred to as LR data. Currently, the mainstream post-processed LR products are released at Level-3 (L3). However, the accuracy level of the SWOT L3 data still requires comprehensive evaluation and analysis. This study primarily focuses on the assessment of SWOT SSH data in both coastal regions and the open ocean.

First, a crossover analysis was conducted using 2-km spatial resolution data over the open ocean and coastal areas. The results indicate that the mean and standard deviation of SSH crossover differences from SWOT are consistent with the accuracy levels of conventional satellite altimetry missions. Second, in situ tide gauge observations were used to evaluate SWOT L3 SSH data with spatial resolutions of 2 km and 250 m within 20 km of the coastline. The results demonstrate good correlation between SWOT L3 data at both resolutions and tide gauge measurements. However, at certain stations—such as those located in regions with complex nearshore bathymetry or around islands and atolls—the correlations are relatively lower.

In addition, an evaluation was performed for SWOT L3 data across different cross-track distance ranges. The results similarly show that, provided valid data are available, SWOT L3 SSH data exhibit good correlation with tide gauge observations across various cross-track distances. Both datasets reveal a consistent pattern of degraded data quality toward the swath edges, while relatively higher quality is observed near the nadir region. Furthermore, a statistical analysis of data loss rates within 5 km of the coastline indicates that the average data loss rate reaches approximately 70% for the 2-km resolution data, whereas the 250-m resolution data exhibit an average loss rate of about 31% within the same nearshore zone.

Finally, comprehensive analysis of all results reveals noticeable differences in data quality between the left and right swaths, which may be related to the satellite’s flight attitude. This issue requires further investigation.

How to cite: Huan, C., Jin, T., Gao, X., Zhou, M., and Shi, J.: Evaluation of SWOT Level 3 Sea Surface Height Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9047, https://doi.org/10.5194/egusphere-egu26-9047, 2026.

Satellite altimetry has made a fundamental contribution to our understanding of ocean circulation during more than three decades, the so-called altimetry era. The wide-swath altimetry concept explored by the SWOT mission has started a new one, the SWOT-era.  As SWOT extends the capability of nadir altimeters to two-dimensional mapping and sampling of the ocean surface at an unprecedented spatial resolution, this has opened the opportunity to constrain better the mesoscale variability, also in areas with a reduced number of the available altimetry missions and small Rossby radii. The results presented in this work rely on a comparison of SWOT retrievals with conventional altimeters and characterize the representation of the mesoscale field in the global ocean emerging from the different altimetry concepts. While this study aims to an assessment of the eddy-induced anomalies at the surface in the global ocean, specific areas of interest along the boundary currents and eddy-rich areas, were selected to investigate the mesoscale contributions to ocean dynamics and thermodynamics (heat transport) building on satellite sensor synergies. The detailed knowledge of the mesoscale eddies (number, size, polarity) is then used to design and train a machine-learning (ML) based detection of the eddies. The accuracy of the results is assessed against the dynamical-based approach applied to the same output fields.

How to cite: Bonaduce, A., Cipollone, A., Broccoli, M., and Raj, R.: Eddy induced anomalies in the global ocean: new insights from wide-swath altimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11662, https://doi.org/10.5194/egusphere-egu26-11662, 2026.

For more than 30 years, satellite altimetry has been providing a unique data record, crucial for many applications such as ocean circulation and the monitoring of sea level rise at global and regional scale. The stringent requirements of such applications set the need for continuous improvement of satellite altimeter products, including those of past missions, in order to get consistent and homogeneous datasets.

The accurate retrieval of sea surface heights from satellite altimetry requires the measured ranges to be corrected for a number of atmospheric and sea state effects.  Amongst these effects, the path delay induced by the water vapor and cloud liquid water, the wet tropospheric correction (WTC), is still a major source of uncertainty, in particular in the long-term sea level trend.

Sponsored by the European Space Agency, the FDR4ALT project aims at generating improved, up-to-date altimetric records for the historical multidisciplinary 35-day repeat missions, ERS-1, ERS-2 and Envisat. To achieve this goal, a new set of wet tropospheric corrections based on the GNSS-derived Path Delay Plus (GPD+) algorithm, has been developed for these missions and is the focus of this study. GPD+ are wet path delays, obtained by data combination through objective analysis, using all available WTC sources, including onboard radiometers, GNSS, external imaging radiometers and an atmospheric model.

Since the GPD+ WTC are combined values, the intercalibration of the various datasets is a crucial step. To this end, a novel method to intercalibrate the various WTC datasets against one of the current best radiometric references, the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), hereafter designated SSMIS, has been developed.

The sensor calibration consists of the adjustment of each dataset to SSMIS using a time-varying set of two parameters (2P), offset and scale factor. For this purpose, 5°x5° grids of WTC mean values for the period of each altimeter cycle and each month, respectively, are computed for SSMIS. Similar grids of mean WTC values for the period of interest are computed for each non-SSMIS sensor, including the MWR onboard each altimeter mission. Using the set of corresponding grid cells for each pair (non-SSMIS, SSMIS) the 2P of the adjustment of each sensor to the SSMIS dataset has been computed. Finally, for the whole study period (1991-2012), ERA5 model and the GNSS dataset have also been adjusted to SSMIS by means of a similar 2P model, with yearly parameters.

This paper summarizes the results, focusing on the impact of the inter-calibration and the added value if these corrections for the FDR4ALT products.

The new calibration revealed to be robust, less sensitive to sampling issues and able to intercalibrate any set of sensors, even with different orbits and sampling patterns. Results show that the new GPD+ WTC are continuous, consistent and intercalibrated datasets, valid over all surface types. In addition to open ocean, they also cover conditions where the measurements from the onboard microwave radiometers are invalid, mostly coastal and polar regions, recovering, on average, 25-30% of the ocean points.

How to cite: Fernandes, M. J., Vieira, T., Aguiar, P., and Lázaro, C.: Improving the Historical ERS and ENVISAT Fundamental Data Records by Means of a New Set of GPD+ Wet Tropospheric Corrections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12562, https://doi.org/10.5194/egusphere-egu26-12562, 2026.

Satellite altimetry provides observations of sea surface height and derived geostrophic currents through the operational DUACS/Copernicus Marine Service Sea Level Thematic Assembly (SLTAC). However, these currents represent only part of the total surface circulation, which also includes ageostrophic components such as wind-driven currents (Ekman and Near-Inertial Oscillations (NIOs)).

To address this limitation, complementary observation-based approaches have been developed to optimally combine satellite and in-situ data, improving the physical realism and temporal resolution of upper-ocean circulation fields. In this study, we use the Multiscale Inversion for Ocean Surface Topography (MIOST) variational tool (Ubelmann et al., 2020) to retrieve both geostrophic and ageostrophic currents. MIOST achieves this by decomposing the signal into representors accounting for different spatial and temporal scales (mesoscale to large-scale) and physical processes (geostrophy, wind-driven currents, NIOs).

Using MIOST, altimetry data are combined with hourly drifter velocities from the Copernicus Marine database (INSITU_GLO_PHY_UV_DISCRETE_MY_013_044, https://data.marine.copernicus.eu/product/INSITU_GLO_PHY_UV_DISCRETE_MY_013_044/description) to represent wind-driven currents, in particular the energetic NIOs, which remain a major challenge for satellite observation (SKIM mission concept, Ubelmann et al., 2021; ODYSEA mission). Previous studies within CNES-funded DUACS project have shown that combining altimetry and hourly drifter data can represent about 30–40% of NIOs in regions with sufficient drifter coverage (e.g., North Atlantic gyre). Additionally, ESA-WOC project has shown that wind reanalyses (ERA5) can predict a substantial fraction (~40%) of NIO variability (Ubelmann et al., 2025).

The objective of this work is to combine these two complementary approaches: starting from prior ageostrophic currents (ESA-WOC) and applying the MIOST variational method to reconstruct geostrophic currents and residual NIO signals contained in drifter observations. Parameter tuning of MIOST modes was required since the statistical mapping is applied to residuals rather than the full signal.

The reconstructed currents are intercompared with existing products, including Copernicus-Globcurrent total surface currents (MULTIOBS_GLO_PHY_MYNRT_015_003) and ESA WOC total surface currents.

Elipot, S., R. Lumpkin, R. C. Perez, J. M. Lilly, J. J. Early, and Sykulski, A.M.: A global surface drifter dataset at hourly resolution, J. Geophys. Res. Oceans, 121, doi:10.1002/2016JC011716, 2016

Ubelmann, C., Dibarboure, G., Gaultier, L., Ponte, A., Ardhuin, F., Ballarotta, M., & Faugere, Y. (2021). Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data. Journal of Geophysical Research: Oceans, 126, e2020JC016560. https://doi.org/10.1029/2020JC016560

Ubelmann, C., Farrar, J. T., Chapron, B., Gaultier, L., Gomez-Navarro, L., Rio, M.-H., and Dibarboure, G.: A data-driven wind-to-current response function and application to ocean surface current estimates, Ocean Sci., 21, 2915–2928, https://doi.org/10.5194/os-21-2915-2025, 2025.

How to cite: Jousset, S., Ubelmann, C., and Dibarboure, G.: Improving Total Surface Currents by Combining Altimetry and In-Situ Observations Using the MIOST Variational Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11828, https://doi.org/10.5194/egusphere-egu26-11828, 2026.

Gridded geostrophic currents derived from satellite altimetry are a cornerstone for investigating ocean surface circulation. However, their effective spatial resolution and dynamical fidelity are highly sensitive to processing choices. Here, we present a comprehensive global intercomparison of several daily, gridded (Level-4) freely available altimetry-based datasets that employ distinct gridding strategies.

Dataset performance is evaluated using a suite of complementary metrics. Spectral diagnostics, including power spectral density and eddy kinetic energy, are employed to quantify the representation of mesoscale variability across a range of spatial scales, dynamical regimes, and energy levels. In addition, a Lagrangian framework is adopted in which virtual drifters are deployed and advected along observed drifter trajectories, enabling robust statistical comparisons of transport and dispersion characteristics among the datasets. The results are consistent across metrics and highlight the strong performance of recently developed products, including neural network–based approaches such as NeurOST, and advanced multi-mission datasets, such as those provided by the Copernicus Marine Environment Monitoring Service (CMEMS) data store.

This intercomparison is conducted within the framework of ESA’s Climate Change Initiative, which aims to expand ESA’s portfolio of Essential Climate Variables. It supports the development of a novel Ocean Surface Heat Flux (OSHF) product, generated to address the observational gaps and limitations inherent in existing OSHF estimates.

How to cite: Juhl, M.-C., Müller, F., Hart-Davis, M., Dettmering, D., Olmedo, E., and Arias, M.: Evaluation of global satellite-derived geostrophic ocean current products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18810, https://doi.org/10.5194/egusphere-egu26-18810, 2026.

Thanks to its current accuracy and maturity, altimetry is considered as a fully operational observing system dedicated to various applications such as climate studies. Altimeter measurements are corrected from several geophysical parameters in order to isolate the oceanic variability and the tide correction is one of the most critical. The accuracy of the tide models has been much improved for the last 30 years, and the most recent FES2022 model allowed reducing the residual errors in shelf and coastal seas and at high latitudes compared to previous version FES2014 and to other state-of-the-art global tide models. However improvements are still needed in these areas were the omission and the modelling error are still significant, but also on the global ocean.

In order to answer the new challenges of the tide correction for HR altimetry, in particular the SWOT mission, which gives access to unprecedent high-resolution sea surface measurements and very narrow coastal areas, a new global tide model FES2026 is currently under development.

This new model will benefit from new tidal frequencies that were not yet included in global tide models, as some minor third order of the tide potential tides and also some complementary non-linear frequencies that are most important in shallow and coastal zones. A dedicated omission error analysis was performed and gives some information about the main non-linear frequencies to take into account. Some evolutions are also envisioned concerning the improvement of the extrapolation procedure at the transition between ocean and land particularly in narrow fjords regions sampled by SWOT KaRIn measurements.

Some comparison with FES2022 and other global tide models (GOT5.6, R. Ray) are proposed. Preliminary validation results using several altimeter missions including SWOT mission, and also some in situ measurements will be presented.

How to cite: Lyard, F., Carrere, L., Marlier, G., Dabat, M.-L., Skandrani, C., and Dibarboure, G.: Preliminary results of the new global barotropic tide model FES2026 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20160, https://doi.org/10.5194/egusphere-egu26-20160, 2026.

The accurate estimation of kilometer-scale ocean surface wind fields is a cornerstone of modern marine meteorology, impacting weather forecasting, offshore wind energy development, and maritime safety. Synthetic Aperture Radar (SAR) remains a primary tool for these estimations due to its high spatial resolution and sensitivity to sea surface roughness. Traditionally, wind retrieval from SAR data relies on Geophysical Model Functions (GMFs) that relate the Normalized Radar Cross Section (NRCS) to wind speed and direction. However, because SAR sensors typically operate with a single fixed antenna, the inversion process is inherently under-determined, frequently requiring ancillary data from Numerical Weather Prediction (NWP) models to resolve wind direction ambiguities. These priors can introduce errors due to spatiotemporal lags or the underestimation of extreme events. To address this, more SAR observables can be exploited to gain independence with respect to model a priori. Here, we focus on the Co-Cross-Polarization Coherence (CCPC), defined as the complex cross-correlation between co-polarized and cross-polarized channels, has emerged as a valuable observable to supplement traditional NRCS measurements.

Recently, the CCPC has been formulated as piecewise log-normal function optimized on an extensive dataset of over 25k Sentinel-1 Interferometric Wide Swath (IW) observations. This modeling ensures the model remains well-behaved and physically consistent even for wind regimes higher than 20 m/s. The utility of the new PGMF-2 has been demonstrated through a Bayesian inversion scheme that integrates the co-polarization, the cross-polarization, and the real and imaginary parts of the CCPC. Monte Carlo simulations confirm that the unique odd-symmetry of the CCPC provides critical directional constraints that complement the even-symmetry of the NRCS. This combination enables wind direction retrieval without the need for NWP priors, particularly in the 7 to 15 m/s range, where the SAR-only inversion outperforms traditional NWP-based methods. 

Moving on from simulation, we applied the inversion on real-world observations. Though a prior remain necessary for some wind field configurations, notably when the wind direction is parallel or ortogonal to the satellite track, as the CCPC is zeroed under these directions, the SAR-retrieved field is coherent with both NWP and scaterrometer data. In addition, the method proposed a unified methodology to introduce new parameters, such as the Imax and the streak direction, to further constrain the SAR-inverted wind field. Through the residual of each component, it also provide a quantitative evaluation of the inversion quality, which is primordial for the detection of pathoological cases and provide warning for the users where the SAR data is noised

This research highlights the potential of using additional SAR observables
CCPC to enable autonomous, high-resolution SAR wind field mapping, which is essential for monitoring rapidly evolving extreme weather systems and optimizing offshore energy resources. This opens new wind retrieval perspectives for current and future SAR missions such as Harmony, ROSE-L and Sentinel-1NG.

How to cite: Husson, R., Colin, A., Mouche, A., Grouazel, A., Nouguier, F., Pinheiro, M., and Longepe, N.: Bayesian wind fields estimates from C-band SAR without NWP prior, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21226, https://doi.org/10.5194/egusphere-egu26-21226, 2026.

Wind vector retrieval from radar measurements plays a fundamental role in ocean remote sensing, providing essential information for weather forecasting, climate studies, and maritime operations. Traditional scatterometer missions, including the ASCAT series aboard METOP satellites, the Chinese HY-2 constellation, and CFOSAT-SCAT on the CFOSAT mission, have established robust operational wind processing chains that have been successfully deployed for decades. These systems rely on well-validated inversion algorithms and ambiguity removal techniques to derive ocean surface wind vectors from radar backscatter measurements. However, the expanding diversity of spaceborne radar systems capable of providing wind information—such as Synthetic Aperture Radars (Sentinel-1), altimetry missions (Sentinel-3, Sentinel-6, SWOT), and emerging constellation concepts—creates a growing need for flexible processing frameworks that can accommodate different instrument geometries, viewing configurations, and measurement characteristics.

In this work, we present GUST (General Utility Scatterometry Tool), a modern Python-based framework designed to provide a unified approach to wind vector retrieval from diverse radar observations. GUST implements established wind-retrieval algorithms, including maximum-likelihood estimation (MLE) inversion with configurable geophysical model functions, and advanced ambiguity-removal techniques such as the two-dimensional variational method (2DVAR), originally developed for operational scatterometer processing. The implementation leverages PyTorch for GPU-accelerated computations, enabling efficient processing of large satellite datasets while maintaining algorithmic transparency and modularity.

In its baseline configuration, GUST fully reproduces the functionality and accuracy of the KNMI Advanced Wind Data Processor (AWDP), the operational standard for ASCAT data processing. Validation against AWDP reference products demonstrates excellent agreement: wind speed RMSE of 0.02 m/s, correlation exceeding 0.99, and approximately 98% agreement in ambiguity selection. These results confirm that the Python implementation maintains the scientific rigor of established Fortran-based processors while providing a more accessible and modifiable codebase.

The key advantage of GUST lies in its flexible architecture. The processing workflow—comprising data reading, quality control, inversion, ambiguity removal, and output generation—is organized as independent, configurable modules. This modular design enables rapid implementation of new retrieval algorithms tailored for emerging observation techniques: cross-polarisation measurements, Doppler-based retrievals, multi-band and multi-angle configurations, or bi-static observation geometries. Furthermore, GUST supports the development of multi-parametric geophysical model functions that incorporate additional ocean state variables—such as sea surface currents and sea state conditions—into the wind retrieval process, advancing beyond traditional wind-only inversions toward more comprehensive air-sea interaction characterisation.

Looking forward, GUST provides a foundation for processing data from next-generation missions with enhanced sensing capabilities, such as Metop-SG and the Harmony mission concept. The Python-based implementation enables straightforward incorporation of new algorithms, including machine learning approaches, and supports collaborative development within the scientific community. By bridging traditional operational processing with modern software practices, GUST aims to accelerate the development of wind-retrieval capabilities in the evolving landscape of ocean observing systems.

Acknowledgement This work was performed as a part of CFOSAT IFREMER Wind and Wave Operation Center (IWWOC) development. The IWWOC is co-funded by CNES and IFREMER.

How to cite: Mironov, A.: GUST: A General Utility Scatterometry Tool for Multi-Platform Ocean Wind Retrieval, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5630, https://doi.org/10.5194/egusphere-egu26-5630, 2026.

Storm surge is one of the primary causes of marine disasters in China, especially tropical storm surges. Numerical model forecasts serve as a critical reference in storm surge operational prediction. The precision of the storm surge simulation is largely contingent upon the accuracy of the input driving forces. The prevailing typhoon storm surge numerical models typically utilize a model wind field derived from tropical parameters as the driving force, ensuring both accuracy and timeliness. However, these statistical models based on the classical wind-pressure relationship or the principle of gradient wind often fail to capture the true asymmetry of the tropical wind field, particularly when the tropical structure is distorted by coastal topography or interactions with other atmospheric systems. Therefore, we upgraded the GPU version of the high-precision operational storm surge forecasting model for the China Sea, by incorporating remote sensing wind data from HY-2B、2C and 2D satellites to assimilate and correct the model wind driven field. Hindcast and forecast cases demonstrated the improvements of calculation results, especially in areas impacted by the right half of the tropical.

How to cite: Fu, X., Gao, Y., Liang, S., and Li, M.: Assimilation of operational storm surge forecasting model with HY-2 satellite wind, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6257, https://doi.org/10.5194/egusphere-egu26-6257, 2026.

The dynamics of river plumes in coastal environments play a key role in sediment transport, water quality, and ecosystem functioning. This study aims to characterize the spatial and temporal variability of the Mondego River plume (Figueira da Foz, Portugal) through satellite observations, assessing how different methodological approaches influence plume detection and interpretation.

High resolution satellite data, including Sentinel-2 and Sentinel-3 were used as the primary source in this study. Environmental datasets from various sources (SNIRH, Copernicus, IPMA) were used to support image interpretation. The analysis covers the period from 2019 to 2025, including a representative set of cloud free satellite images. Different atmospheric correction approaches, such as Sen2Cor (ESA L2A) and Dark Spectrum Fitting (ACOLITE), were applied to evaluate the impact of atmospheric pre-processing on plume detection and analysis. Several plume detection techniques were tested, including reflectance and turbidity thresholds, spectral distance and unsupervised clustering methods. This analysis was conducted in two phases: an initial phase following well-documented methodologies successfully applied in previous studies in literature, followed by an exploratory approach in which case-specific tests were designed to improve plume detection. 

Results reveal pronounced seasonal and event driven variability in plume extent and dispersion, consistent with variations in river discharge, tides and wind forcing. Differences between atmospheric correction methods lead to variations in plume detectability and estimated spatial extent, highlighting the sensitivity of satellite derived products. Clustering based approaches capture plume morphology and spatial continuity, while threshold based methods provide rapid and easily interpretable estimates of plume extent. 

Overall, this study demonstrates how satellite based plume characterization strongly depends on methodological approaches and pre-processing strategies, providing a reproducible framework for monitoring plume dynamics. This work is relevant for coastal management, while also underlining limitations related to atmospheric conditions and ancillary data quality.

How to cite: T. Silva, B., B. Oliveira, P., and A. Silva, P.: Spatial and Temporal Variability of the Mondego River Plume: A Satellite Based Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10289, https://doi.org/10.5194/egusphere-egu26-10289, 2026.

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a transformative remote sensing technique for ocean geophysical parameter monitoring. However, existing wind speed retrieval algorithms often encounter accuracy bottlenecks due to an incomplete accounting of non-local swell modulations in different sea state conditions. This study identifies that, beyond Significant Wave Height (SWH), the swell period is a critical driver of GNSS-R signal variations, exerting an inherently non-linear influence on sea surface roughness across different wind speed regimes. To address this, we propose a refined retrieval framework. First, the swell period is introduced as a corrective parameter to calibrate scattering cross-section deviations induced by non-local waves, addressing the mismatch between local wind stress and surface roughness. Second, the instantaneous change rate of the Normalized Bistatic Radar Cross Section (NBRCS) is incorporated as a dynamic sensitivity indicator. This feature effectively enhances the model’s capability to distinguish between developing and fully developed seas, providing a temporal dimension to the traditionally static retrieval approach. A Multi-Layer Perceptron (MLP) architecture was optimized using CYGNSS mission data for end-to-end estimation. Validation against ERA5 reanalysis data demonstrates that the integration of swell dynamics and NBRCS temporal variations significantly enhances retrieval robustness across all wind speed ranges. While the model achieved a correlation coefficient (R) of 0.86 and an RMSE of 1.37 m/s across all ranges, the proposed model improves these metrics to R = 0.87 and RMSE = 1.32 m/s. Specifically, the model exhibits improved performance in both the high wind speed range (> 15 m/s), where the RMSE decreased from 2.79 m/s to 2.37 m/s (a 15% improvement), and the low wind speed range (< 10 m/s), with an RMSE of 1.21 m/s. These results underscore the necessity of accounting for swell-related wave age and temporal signal variations to achieve high-precision GNSS-R sensing under diverse and complex global sea conditions.

How to cite: Xian, H. and Jin, T.: A Refined Spaceborne GNSS-R Wind Speed Retrieval Framework Considering Swell Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16293, https://doi.org/10.5194/egusphere-egu26-16293, 2026.

The Arabian Gulf (Gulf) is a dynamic marine ecosystem and optically complex marginal sea, where the retrieval of chlorophyll-a (Chl-a) from ocean color observations is challenging due to intense atmospheric dust, high suspended sediments, and shallow bathymetry. Nevertheless, satellite-derived Chl-a, widely used as a proxy for marine primary productivity, is one of the key essential climate variables for monitoring long-term marine ecosystem variability and climate change. Given the absence of a single satellite mission spanning the full historical record of ocean color data, the construction of continuous, multi-decadal Chl-a time series relies on the integration of multiple sensors, highlighting the need for careful regional evaluation of available products. This study assessed the performance and consistency of widely used single-sensor and multi-sensor merged Chl-a products in the Arabian Gulf, including MODIS-Aqua, the Ocean Colour Climate Change Initiative (OC-CCI), and GlobColour datasets. The assessment was based on statistical comparisons with available in situ Chl-a measurements and analyses of spatiotemporal variability. Temporal consistency was also examined during both common operational periods and continuous overlapping lifespans of the datasets, with analyses covering time period from September 1997 to September 2025.

The results showed strong and stable correlations among the products, indicating robust temporal coherence in the Gulf. All products demonstrated valuable insights into Chl-a variability; however, notable differences were observed in spatial pattern and long-term trends, particularly over coastal and shelf regions. OC-CCI provided better spatial and temporal coverage with greater overall stability, making it particularly suitable for long-term environmental monitoring in the Gulf. Trend analysis revealed contrasting spatial patterns among the products, with OC-CCI indicated an overall declining Chl-a trend with localized coastal increases, while GlobColour exhibited increasing trends along the northern and southern coasts and a declining trend along the eastern coast. These differences are likely linked to known quality limitations in the early phases of merged datasets, especially during periods dominated by single-sensor contributions. Consistent with this, the OC-CCI record during 1997-2002 also shows regional discrepancies and less coherent trend behavior, which become substantially reduced after 2003, coinciding with increased sensor overlap and improved temporal consistency. This study highlights the necessity of regional validation of global ocean-color products in optically complex environments and identifies the most reliable dataset for developing long-term Chl-a records in the Gulf, supporting improved assessments of marine productivity and climate change impacts.

How to cite: Shafeeque, M., Saleem, A. A., Chaitanya, A. V. S., Lateef, S., and Albert, J.: Inter-comparison of Ocean Color chlorophyll products for assessing the marine productivity of the Arabian Gulf, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20793, https://doi.org/10.5194/egusphere-egu26-20793, 2026.

The ocean archives massive, stable remote sensing datasets, and leveraging these data to achieve intelligent real-time recognition of marine organisms has become a core task in the field of marine remote sensing. However, existing object recognition algorithms primarily focus on determining the location of objects, neglecting the demand for refined object recognition. When these algorithms that only identify location and category applied to marine remote sensing, they fail to meet the requirements of marine fisheries, species conservation, and precise underwater operations. To expand the application scenarios of marine target recognition, we propose a Key Point Refined Network (KPR-Net) for fish contour recognition, a two-stage adaptive target detection algorithm that first determines the positions of target feature points through feature extraction and sufficient fusion processing, then delineates the target contour via simple topological relationships. Our proposed KPR-Net efficiently and accurately predicts the positions of marine target feature points by integrating a self-attention mechanism with multi-feature aggregation. Furthermore, to incorporate biological characteristics and enhance the detection performance of fish targets, we take the natural contour features of fish as constraints, accurately determine the arrangement of these vertices based on the positions and directions of the target feature points, and sequentially connect them according to the determined arrangement to obtain the target contour. Experiments on multiple challenging marine datasets demonstrate the accuracy of our proposed method in multi-category target detection tasks. Particularly in the refined presentation stage of recognition results, the determination of target contours enriches the output content of the target detection algorithm, providing more detailed and comprehensive recognition information.

How to cite: Rao, W., Jeboyedoff, M., and Chen, G.: An Improved Object Recognition Algorithm via Feature Point: A Case Study of Fish Contour Recognition in Marine Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5193, https://doi.org/10.5194/egusphere-egu26-5193, 2026.

Meteotsunamis, as the name suggests, are atmospherically generated tsunamis, most often formed during fast passage of a mesoscale atmospheric disturbance, such as convective storm capable of ducting gravity waves and rapidly changing surface air pressure. Energy from the atmosphere is transferred to the water body and the resulting high-frequency (T < 2h) sea level disturbances can exceed one meter if amplified by Proudman or Greenspan resonance. These extreme events can cause a substantial damage to coastal communities and may even lead to human causalities, which have been reported around the world. Thus, it is vitally important to be able to forecast such events in a timely manner. However, such meteotsunami events are hard to predict due to limited spatial coverage of tide gauges as well as due to convective storms, i.e., their shape, speed, propagation direction and intensity changing in time, to which—on top of coastal ocean bathymetry modulations—intensity of meteotsunamis is dependent. Previous studies have shown that various hazardous natural phenomena—such as earthquakes, volcano eruptions, strong convective storms and tsunamis—can produce detectable ionospheric disturbances measurable by perturbations of the ionospheric total electron content (TEC) opening a possibility for earlier warning. To date, only one study has investigated ionospheric TEC perturbation associated with meteotsunamis, focusing on an event along the eastern coast of the United States, with encouraging result. To check whether similar approach could be applied for other parts of the globe, this study investigates a meteotsunami observed at Australian south-east coast which formed on 30 April 2020. Conditions for the upward propagation of meteotsunami-generated atmospheric gravity waves (AGWs) into the ionosphere are checked by using linear gravity wave theory. Observational evidence is assessed by utilizing space-based radio occultation (RO) measurements from Global Navigational Satellite System (GNSS), Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures and ground-based GNSS TEC observations. Our result reveals evidence of free propagation of meteotsunami-generated AGWs into the upper atmosphere along with associated variability in ionospheric TEC of approximately 1 TECU. These findings support the potential of ionospheric observations as a complementary tool for meteotsunami signal detection and possibly early warning.

How to cite: Radovan, A. and Vilibić, I.: A sea in the sky? Evidence of meteotsunami signatures in the ionosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4857, https://doi.org/10.5194/egusphere-egu26-4857, 2026.

Antarctic open-ocean polynyas (OOPs) are critical windows for air-sea interaction, significantly enhancing heat exchange and driving Antarctic Bottom Water (AABW) formation. While their importance is recognized, their long-term spatiotemporal evolution and the underlying subsurface drivers remain incompletely understood. This study identifies recurrent deep-water OOPs and reveals a pronounced longitudinal asymmetry and complex subsurface dynamics through a refined 20-year analysis.

Using the DEEP-AA daily polynya edge dataset (2003–2022), we tracked open-water features during austral winters (April–October). By applying rigorous spatiotemporal filters—excluding short-lived (<20 days), small-scale (<100 km²), or coastal-influenced features—we identified 16 primary OOPs. Among these, eight were classified as "deep-water OOPs" based on their location over abyssal depths (>3,500 m) or major topographic features, such as seamounts and mid-ocean ridges.

Our results highlight a strong clustering of deep-water OOPs within the 90°W–90°E sector, governed by coupled dynamic-thermodynamic processes. In the 55°S–65°S latitudinal band, Antarctic Circumpolar Current (ACC) shear amplifies Ekman transport and mesoscale eddy activity. Topographic features, such as Maud Rise, induce flow divergence that sustains surface openings. In contrast, the northward deflection of the ACC west of 90°W promotes sea-ice expansion in the Ross and Amundsen Seas. These OOPs act as vital conduits for AABW formation; topographic uplift facilitates the upward transport of warm Circumpolar Deep Water (CDW; T > 0°C, S ≈ 34.6 psu) into the mixed layer, effectively inhibiting ice growth.

A key novelty of this study is the identification of "Silent Periods"—intervals where the surface appears frozen (days to months) due to extreme winter cooling, yet subsurface heat transport remains active. We found that residual thermal anomalies create an "Oceanic Heat Memory" effect, which preconditions the polynya for rapid reactivation. Conventional sea-ice concentration (SIC) threshold methods fail to capture these subsurface signals, consistently underestimating both polynya persistence and total ocean-to-atmosphere heat fluxes.

These findings demonstrate that seabed topography, environmental conditions, and ocean circulation are the primary determinants of deep-water polynyas distribution. By elucidating the mechanisms of topographic preconditioning and the limitations of surface-only observations, this work provides essential insights for improving ice-ocean coupling in Earth System Models and refining projections of Southern Ocean climate change.

How to cite: Luo, X. and Fan, X.: Spatial Heterogeneity and Multi-Decadal Dynamics of Antarctic Deep-Water Polynyas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3297, https://doi.org/10.5194/egusphere-egu26-3297, 2026.

Accurate estimates of ocean surface heat fluxes (OSHF) are essential for assessing and improving climate projections and supporting adaptation strategies, yet direct measurements are challenging, costly, and not feasible at global scales. The GCOS Implementation Plan 2022 emphasizes the urgent need to enhance estimates of latent and sensible heat fluxes, recommends greater use of satellite data, and highlights that current in-situ observing systems, such as Argo, are insufficient to provide the high-resolution data required for climate modeling and model validation.

The CCI OSHF project addresses this gap by generating a new satellite-based OSHF product aiming at meeting the climate community requirements for spatial and temporal resolution, timeliness, uncertainty, and long-term stability. By relying on fundamental physical principles rather than parameterizations and leveraging multiple satellite missions and in-situ observing networks, the product aims at reducing uncertainties.

After one year of the project, we will present the user requirements outcomes from the dedicated user consultation action, describe the methodology used to generate the product, and show preliminary results on the performance of the first 15-year version of the new satellite-derived OSHF dataset.

How to cite: Olmedo, E., Arias, M., Bergas-Ques, J., Cornes, R., González-Gambau, V., Haimberger, L., Hart-Davis, M., Juhl, M.-C., Kent, E., Mayer, M., Merchant, C., Müller, F., Ruiz-Sebastián, A., Sagués, A., Storto, A., Turiel, A., Winkelbauer, S., Yang, C., and Pinnock, S.:  CCI-OSHF: A new ESA Climate Change Initiative to enhance ocean surface heat fluxes estimates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17849, https://doi.org/10.5194/egusphere-egu26-17849, 2026.

Distributed Acoustic Sensing (DAS) provides dense, distributed measurements along subsea fibre-optic cables and has demonstrated potential for passive detection, e.g., of surface vessels, whales, and similar objects. Beyond detection and localisation, the spectral content of the acoustic signals contains information about the object, such as propulsion, machinery, and operational state for ships. In hydrophones, such information is often extracted using spectral and modulation-based techniques, including Low-Frequency Analysis and Recording (LOFAR) and Detection of Envelope Modulation on Noise (DEMON). However, the direct transfer of these methods to DAS is non-trivial due to differences in frequencies, coupling conditions, and the limited acoustic bandwidth typically available in DAS systems.

In this study, we investigate how ship radiated noise can be spectrally characterised using DAS by adapting analysis concepts originally developed for hydrophone-based passive acoustics. We focus on frequency bands accessible to DAS low-frequency tonal components are observable. A preprocessing pipeline is applied to enhance signal-to-noise ratio and suppress non-acoustic contributions. Within this framework, DEMON-inspired envelope analysis is explored in a form compatible with DAS bandwidth constraints, alongside direct low-frequency spectral methods.

To place the extracted spectral features in a physical and operational context, the DAS-derived signatures are compared with ship information from AIS data and auxillary ship information. This comparison enables assessment of how observed spectral characteristics relate to known ship properties and operating conditions, and clarifies which elements of classical underwater radiated noise theory are transferable to DAS observations.

By focusing on spectral characterisation rather than localisation performance, this work aims to bridge the gap between traditional hydrophone-based ship acoustics and emerging DAS-based maritime monitoring, providing a methodological foundation for interpreting DAS detections in terms of vessel class and behaviour.

How to cite: Soerensen, K. A., Orellana, L., Pedersen, H., Nezhad, K., and Heiselberg, H.: Spectral Characterisation of Undewater Radiated Noise Using Distributed Acoustic Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20071, https://doi.org/10.5194/egusphere-egu26-20071, 2026.