Search Results (270)

Regional sea-level trends derived from satellite altimetry deviate substantially from the global mean, but the relative roles of externally forced change and internally generated climate variability remain difficult to separate from the short satellite record. Here, we examine the 32-year Data Unification and Altimeter Combination System (DUACS) gridded multi-mission satellite altimetry product (January 1993–December 2024) together with 100 100-year samples from an unforced Community Earth System Model (CESM) pre-industrial control simulation. Empirical orthogonal function (EOF) analysis of satellite sea-level anomalies reveals a leading mode explaining 10.9% of total variance, with an Interdecadal Pacific Oscillation (IPO)-like dipolar pattern and high correlation with the IPO index (r = 0.92). A similar IPO-like mode appears consistently in the unforced CESM samples. Because previous large-ensemble studies indicate that the externally forced sea-level response is generally broader and structurally distinct from this dipolar internal mode, this agreement supports the interpretation that the satellite-observed leading pattern is strongly consistent with internally generated variability, although a partial forced contribution, particularly in the tropical Pacific, cannot be excluded. Based on CESM simulations, the empirical contribution of internal variability to regional trend uncertainty decreases approximately inversely with record length. The resulting location-specific estimate can be scaled by the local EOF amplitude and is largest in regions where the dominant internal-variability mode has large amplitudes, including the western tropical Pacific and Indian Ocean. However, this estimate represents only the internally generated component inferred from a single unforced CESM simulation. It does not include DUACS mapping errors, inter-mission calibration uncertainty, geophysical correction uncertainty, glacial-isostatic-adjustment-related bias, or uncertainty in the forced sea-level response. Thus, this study provides a model-based framework for estimating the internal-variability contribution to regional sea-level trend uncertainty, rather than a formal detection-and-attribution separation or a complete uncertainty bound for satellite-altimeter-derived regional sea-level trends. Full article

Over the past three decades, five satellites have succeeded one another on the reference orbit, building the longest continuous climate record of global sea level measurements. Its continuity is ensured thanks to tandem flights between consecutive satellites. In this paper, we demonstrate that the first satellite of the Sentinel-6 series (Sentinel-6 Michael Freilich) has enabled the detection of a processing anomaly in the Jason-1/2/3 ground segment. An inconsistency in the altimeter range reconstruction has been identified, causing its underestimation by 3.65 mm. At certain latitudes, determined by the satellite’s orbital velocity, the altimeter range shows no effect from the anomaly. For the reference orbit, the range is not affected at the poles, around the equator and, for ascending tracks, at 40° S. All Jason Geophysical Data Record (GDR) versions prior to GDR-G are impacted by the described processing anomaly. While a full reprocessing of the Jason data with the GDR-G standard is pending, this paper presents a latitudinal empirical correction to be applied to Jason datasets generated with GDR-F and earlier ground segments. This correction, to be applied on the altimeter range, is derived from one month of patched Jason-3 data and is intended for reference orbit only. Additionally, SWOT Nadir ground processing is also affected by the same processing error and has been corrected from the GDR-S2 version onward. Finally, our analysis shows a negligible impact of this processing anomaly on Jason Level-2-derived products, models and metrics. Full article

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R² = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R² = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline. Full article

To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables high-precision SSWS retrieval from GF-3B data. Conventional SAR-based SSWS retrieval models typically rely on pointwise mapping relationships, which overlook the spatial characteristics inherent in dynamic sea surface wind fields. To overcome this limitation, this study proposes an attention-guided dual-branch feature-fusion network (ADBFF-NET). The first branch, implemented as a backpropagation neural network (BPNN), learns nonlinear mappings between the normalized radar cross-section (NRCS, σ⁰), incidence angle, azimuth look direction, and wind vectors (speed and direction). The second branch, designed as a residual convolutional neural network, extracts spatial features of wind fields. An attention mechanism fuses the outputs of both branches, thereby enhancing retrieval accuracy. Experiments conducted with GF-3 series satellite data were validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5), Advanced Scatterometer (ASCAT) wind fields, and altimeter-derived wind speeds. The results indicate that the SSWS retrieved from GF-3B SAR data using the corrected calibration constants achieve a root mean square error (RMSE) of 1 m/s against ERA5 wind speeds, representing an approximately 40% reduction compared with the RMSE obtained using the original calibration constant. Furthermore, compared to ERA5 and ASCAT data, the RMSE of the wind speeds retrieved by the ADBFF-NET model reaches 1.17 m/s and 1.03 m/s, respectively. Full article

The acquisition of significant wave height (SWH) in coastal seas is significantly important to human activities. The Gaofen-3 (GF-3) satellites, comprising GF-3, GF-3B and GF-3C, are independently developed operational SAR of China, capable of providing high-precision, high-resolution, multi-polarization coastal ocean wave observations. In order to obtain SWH in coastal seas, the retrieval of SWH using Quad-Polarization Stripmap (QPS) mode data from GF-3 satellites based on the deep learning method is implemented in this study. Furthermore, to obtain more SWH data, the polarization ratio model was applied to the Fine Stripmap (FS) mode data and Ultra Fine Stripmap (UFS) mode data to extend the model application. Comparisons with ECMWF Reanalysis v5 (ERA5) wave heights show that the QPS mode SWH retrieval achieves a root mean square error (RMSE) of 0.33 m. For the FS mode, the RMSE is 0.44 m (vs. ERA5) and 0.52 m (vs. altimeter). For the UFS mode, the RMSE is 0.39 m (vs. ERA5). Evaluation results indicate the feasibility of the proposed method for coastal SWH retrieval. Full article

This study uses satellite altimeter data from the new AVISO dataset to investigate the coupling between sea level variability in the Sicily Channel and the Ionian Sea. The dataset spans the last three decades (1993–2024) and provides high spatial resolution coverage of the Mediterranean Sea (1/16°, or approximately 7 km). We analyze the variability of the sea surface height through Empirical Orthogonal Function and Singular Value Decomposition techniques applied to the Absolute Dynamic Topography. While the dominant modes of long-term variability reflect the known dynamics of the North Ionian Gyre, the singular value analysis allows us to identify a coherent spatial structure extending from the Sicily Channel to the Northern Ionian Sea. This provides the first observation-based, robust evidence of a dynamical coupling between the two basins, indicating that in the last thirty years the Northern Ionian Gyre is part of a broader, dynamically connected regional system integrating flows from the Sicily Channel. These findings are consistent with previous work, based on a hindcast simulation covering 1980–2010, in which we highlighted the key role of the Atlantic Ionian Stream in shaping interannual to decadal variability in the Northern Ionian Sea. Here, we extend the analysis to the present day, providing the most up-to-date, observation-based assessment of the regional dynamics. Full article

Mesoscale eddies are highly active around the Kuroshio in the East China Sea (ECS), serving as a crucial component of the ECS’s complex dynamic environment. However, the long-term variation in mesoscale eddies in this region has not been systematically investigated. Based on daily satellite altimeter data spanning from January 1993 to December 2023, this study comprehensively investigates the trend characteristics of mesoscale eddies in the ECS during this period, using eddy metrics such as Eddy Kinetic Energy (EKE) and eddy polarity probability. EKE in the ECS is primarily high around the Kuroshio, exhibiting a significant increasing trend. This upward trend is more pronounced in summer, autumn, and winter, all of which pass the significance test. From the statistics of coherent mesoscale eddies, cyclonic and anticyclonic eddies show opposite trend characteristics: cyclonic eddies display trends of decreasing number and weakening intensity, while anticyclonic eddies exhibit trends of increasing number and strengthening intensity. Energy transfer from the background flow makes a certain contribution to the aforementioned trends, but is relatively complex. The opposing trend characteristics exhibited by eddies of different polarities are related to the northwestward shift of Kuroshio Current. The nonuniform changes in cyclonic and anticyclonic eddies could affect the regional patterns of ocean circulation and biogeochemical responses to future climate change. Full article

Equipped with an Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 (Ice, Cloud and land Elevation Satellite-2) is a photon-counting laser altimetry mission with strong potential for nearshore bathymetry. In this study, a novel filtering and bathymetric method termed a segmented adaptive filtering bathymetry has been proposed. Sea-surface photons are identified from peaks in the elevation-density histogram, enabling separation of surface and seafloor photons. The seafloor photons are then partitioned into along-track segments, where seafloor signal photons are extracted using an adaptive elliptical kernel whose parameters and orientation are determined from local density patterns and seafloor slope. The seafloor profile is obtained by polynomial fitting, and nearshore depth is estimated from the elevations of the surface and seafloor signal photons. To ensure and improve the accuracy and reliability of the proposed method, ICESat-2 data from Qilianyu Islands at the South China Sea and West Island at the Florida Keys of the United States were adopted to perform experiments. Furthermore, the bathymetric results obtained by ICESat-2 datasets at different experimental areas were compared with the reference bathymetry obtained by the airborne light detection and ranging (LiDAR) bathymetry (ALB) system. Finally, the bathymetric accuracy validation and assessment were performed. The highest accuracy of root mean square error (RMSE) and coefficient of determination (R²) has reached 0.37 m and 98%, respectively. The accuracy validation of bathymetric results at different study areas demonstrated that the method proposed in this study can automatically and effectively achieve high-precision nearshore bathymetry and topographic surveys. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

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. Full article

Satellite altimeters provide global observations of significant wave height (SWH, in m), yet buoy-based validation is affected by representativeness errors and sampling mismatches. This study develops a consistent framework for validating and scaling HY-2B SWH that integrates nearest-point spatiotemporal collocation, sea-state-binned diagnostics, three complementary calibration schemes (bias correction, ordinary least-squares (OLS) linear regression scaling, and machine-learning residual correction), and Extended Triple Collocation (ETC) for sensor-independent uncertainty estimates. The dataset includes HY-2B SWH, National Data Buoy Center (NDBC) buoy records, seven buoys in the Taiwan Strait, and the sea surface significant wave height (VHM0, in m) from the Copernicus Marine Environment Monitoring Service (CMEMS) Global Wave Reanalysis. Sensitivity tests show that tightening the collocation radius from 100 to 25 km reduces scatter (RMSE/STD) while preserving near-zero bias; correlations remain ≥0.97 for 25–50 km but degrade at larger windows, underscoring representativeness effects. Error metrics increase monotonically with sea state, whereas mean biases remain small. ETC applied to HY-2B, NDBC, and CMEMS yields random error standard deviations of 0.158, 0.147, and 0.179 m, respectively, with squared correlation coefficients (${\rho }^2$) of approximately $0.96$–$0.98$ for all systems. Scaling experiments reveal a data-quality-dependent behavior: for NDBC matchups, HY-2B already agrees closely with buoys (e.g., RMSE ≈ 0.24 m), and additional scaling brings no benefit; for the Taiwan Strait buoys, all three schemes improve agreement (RMSE ≈ 0.41 m; correlation ≈ 0.95), with the residual machine-learning model providing the largest reduction in random error. The results support a practical protocol for HY-2B SWH validation: a 30 min/25–50 km window, modest outlier screening, and selective use of linear or residual corrections depending on buoy network and environment. Full article

Conventional altimeter satellites, such as TOPEX/Poseidon and Jason series, can identify ocean mesoscale eddies (MEs) but cannot effectively distinguish submesoscale eddies (SMEs) due to horizontal resolution limitations. The emergence of the Surface Water and Ocean Topography (SWOT) satellite has enabled the resolution (or detection) of SMEs. At present, Data Unification and Altimeter Combination System (DUACS) (MEs-resolving) and SWOT (SMEs-resolving) satellites operate concurrently in orbit, however a systematic comparison and analysis of their observational outputs has yet to be conducted. Using a closed-contour scalar analysis method, this study identifies SMEs in the tropical western Pacific Ocean and compares the results with those from the dataset. The latitude-dependent Rossby deformation radius is employed to differentiate MEs from SMEs. For MEs, SWOT detects 176 per 10.5-day sub-cycle, while DUACS detects 162, which are roughly equivalent. For SMEs, SWOT identifies 273 per sub-cycle, far exceeding the 13 detected by DUACS. For amplitudes, DUACS measures 5.22 cm and 3.67 cm for MEs and SMEs, respectively, while the values reported by the SWOT satellite are 6.13 cm and 4.49 cm. In both datasets, cyclonic eddies are more prevalent in all cases except for the SMEs detected by SWOT, where anticyclonic eddies slightly outnumber cyclonic eddies. Additionally, during the trial operation and scientific orbit phases, SWOT is able to resolve 29 SMEs per orbit. The results indicate that high-resolution data can distinguish phenomena that conventional satellite altimeters cannot capture, providing valuable references for the analysis and application of SME characteristics. Full article

This study investigated the vertical structure of an anticyclonic eddy (AE) in the northern South China Sea (SCS) in August 2017 and its response to Typhoon Hato using underwater glider and satellite altimeter data. Additionally, comparative experiments with and without typhoon forcing were conducted using the Regional Ocean Modeling System (ROMS) for supplementary analysis. The observational results reveal that the maximum temperature and salinity differences between the center and edge of the AE did not occur at the sea surface but near the 100 m depth. The typhoon caused a significant temperature decrease above 200 m, with the maximum cooling (~2 °C) occurring near 50 m. Near this depth, salinity initially increased due to upwelling but later decreased due to surface mixing. The most pronounced cooling and salinity changes occurred one day after the typhoon passage, followed by a gradual deepening of the mixed layer over the next four days, with conditions below the mixed layer largely returning to pre-typhoon states. Numerical modeling quantitatively assessed the typhoon’s impacts. Upwelling rapidly intensified during the typhoon’s passage, the typhoon’s wind stress decreased kinetic energy at the AE site, and the input of positive vorticity reduced absolute vorticity, disrupting the surface AE structure. The flow field adjusted faster than temperature and salinity, with surface currents and the AE structure largely recovering within two days after the typhoon’s passage. These findings highlight the multifaceted impacts of typhoons on AEs and provide critical insights for predicting the evolution of mesoscale oceanic structures under extreme weather events. Full article

Monitoring freshwater resources is essential for assessing the impacts of drought, water management and global warming. Spaceborne LiDAR altimeters allow researchers to obtain water height information, while water area and precipitation data can be obtained using different satellite systems. In our study, we examined 5 years (2018–2022) of data concerning the Euphrates–Tigris Basin (ETB), one of the most important freshwater resources of the Middle East, and the water bodies of both the ETB and the largest lake of Türkiye, Lake Van. A multi-sensor study aimed to detect and monitor water levels and water areas in the water scarcity basin. The ATL13 product of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was used to determine water levels, while the normalized difference water index was applied to the Sentinel-2 optical imaging satellite to monitor the water area. Variations in both water level and area may be related to the time series of precipitation data from the ECMWF Reanalysis v5 (ERA5) product. In addition, our results were compared with global HydroWeb water level data. Consequently, it was observed that the water levels in the region decreased by 5–6 m in many reservoirs after 2019. It is noteworthy that there was a decrease of approximately 14 m in the water level and 684 km² in the water area between July 2019 and July 2022 in Lake Therthar. Full article