Search Results (823)

In March 2022, the FAO Food Price Index peaked at 159.7 as climate shocks collided with geopolitical disruption, pushing global hunger past 735 million and exposing how deeply climate variability penetrates the economics of agri-food systems. Yet the imprint of ocean–atmosphere oscillations on global food prices—the central economic signal of the agri-food system—has not, to our knowledge, been mapped systematically in the frequency domain. This study delivers, to our knowledge, one of the first multi-oscillation cross-spectral analyses of the climate–food price nexus, matching 7 climate indices across the Pacific, Atlantic, and Indian Ocean basins with 5 disaggregated FAO Food Price Index sub-components over 432 monthly observations (1990–2025), verified through 6 robustness checks, including surrogate data testing. Four findings carry direct policy relevance. ENSO indicators lead global food prices by three to four months with a 100% surrogate test pass rate—one of the cleanest actionable climate–price signals documented to date. The Indian Ocean Dipole leads prices by roughly one to two years (cross-correlation peak at sixteen months, though the peak is broad and not sharply localised within that window), extending the early warning horizon well beyond the ENSO signal. The apparent Atlantic Multidecadal Oscillation–price correlation (r ≈ +0.60) is revealed to be a common-trend artefact. Vegetable oils are the most consistently climate-exposed commodity chain across the seven oscillations; sugar and meat, often assumed less climate-sensitive, in fact show strong coherence with specific oscillations (sugar with the Indian Ocean Dipole and meat with the Southern Oscillation Index), indicating that commodity-level exposure is oscillation-specific rather than uniform and reflects each commodity’s position in the production-to-consumption chain—short-cycle, thinly buffered commodities transmit weather shocks to price quickly, while feed-market intermediation delays and smooths the pass-through for livestock. These results provide the empirical foundation for integrating real-time monitoring of climate oscillations into food system governance—a low-cost policy innovation that aligns economic stability objectives with climate adaptation goals, strengthens the resilience of agri-food value chains, and supports progress towards Sustainable Development Goal 2 (Zero Hunger). Full article

Sedimentation is eroding the water security of reservoirs in hydrologically active basins: the Poechos reservoir (Peru) has lost 62% of its original 887.7 hm³ capacity in 48 years, yet its Elevation–Area–Volume (EAV) curve is refreshed only by bathymetric surveys at decade-plus intervals, compromising flood regulation and the water supply for over 100,000 ha of farmland. To close this gap, we propose an integrated, low-cost, fully reproducible framework that reconstructs the EAV curve from freely available satellite data: Sentinel-1 SAR (287 acquisitions, 2021–2026), PlanetScope imagery as ground truth (23 dates), and Surface Water and Ocean Topography (SWOT) altimetry (53 validated passes, 2023–2026). Water surfaces were delineated with a deep learning segmentation model (Feature Pyramid Network with an InceptionV4 encoder), selected among nine architecture–encoder combinations and calibrated to a 0.64 decision threshold, achieving a 90.66% Intersection over Union (IoU) and a 95.10% F1 score; a stochastic quantile mapping algorithm then asynchronously coupled the area and elevation series. The resulting EAV curve matched daily operational records from Peru’s National Water Authority (ANA) with high precision (NSE = 0.94, R² = 0.96, and RMSE = 25.93 hm³); the residual bias (BIAS = −11.23 hm³) reflects active sedimentation unaccounted for in the official curve. This bias peaked at an accumulated deficit of 24.5 hm³ during the 2023–2024 hydrological year (3.5 hm³/year), of which up to 19.6 hm³ is attributed to the 2023 Yaku cyclone as a phenomenologically scaled upper-bound estimate (9.8–19.6 hm³ across 40–80% attribution fractions), since SWOT was not yet operational during the event. Updating every 21 days under any weather and requiring no new field campaigns beyond the baseline bathymetric anchor, the trained ensemble was further transferred zero-shot to three additional reservoirs (San Lorenzo, Tinajones, and Gallito Ciego), demonstrating a scalable path from infrequent static assessments to near-continuous, dynamic monitoring of water storage. Full article

This study focuses on the Significant Wave Height (SWH) retrieval from the Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite by combining backscattering and interference characteristics. To this end, the backscattering-related and interference-related parameters were jointly used as inputs to develop a machine learning model. Here, the backscattering-related data include normalized radar cross-section (NRCS), incidence angle, and the image spectra parameters extracted from KaRIn Level 1B (L1B) data, while the interference-related data correspond to the Level 2 (L2) volumetric correlation, which characterizes the influence of ocean wave scattering on interferometric coherence. The machine learning model is built upon a Multi-Layer Perceptron (MLP), which serves as a nonlinear fitting tool. SWH retrievals from the proposed method and the existing L2 SWH product as a reference were validated by the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, Haiyang2C (HY2C) and Haiyang2D (HY2D) altimeter data, and National Data Buoy Center (NDBC) buoy data. Validations show that both KaRIn SWH have a good agreement with collocations in terms of correlation coefficient (COR), BIAS and root mean square error (RMSE). Moreover, the retrieval accuracy from the proposed method (with an RMSE of about 0.29 m) is better than that of the L2 product (with an RMSE of about 0.46 m) when validated against the collocated ECMWF datasets. Ablation analysis further confirms that image spectra parameters and volumetric correlation are the dominant factors driving the retrieval accuracy improvement, with notable contribution differences among the sub-parameters of spectral features. This performance gain arises from the complementary physical mechanisms of backscattering and interferometric observables, which describe sea state information from independent dimensions. These accurate SWH retrievals can help correct sea state biases for collocated KaRIn sea surface height products and complement wave products from other satellite sensors. Full article

As crucial weather-scale systems widely affecting the global marine-atmospheric boundary layer, cyclones exert a regulatory effect on aerosols in the marine boundary layer through interrelated physical and chemical processes, including dynamic uplift, strong wind forcing, precipitation scavenging, and cloud microphysical interactions. Following an overview of aerosol properties in the marine boundary layer and synoptic cyclone characteristics, this paper reviews the full-process regulation mechanisms and mutual feedback effects of tropical and extratropical cyclones on aerosol generation, long-range transport, and removal, integrating the latest advances in observational, numerical, and theoretical studies. Cyclone-driven aerosol generation has two key pathways: mechanical fragmentation of sea surfaces in cyclones’ strong wind cores, emitting sea salt aerosols of varying particle sizes, and cyclone-induced disturbances triggering photochemical and heterogeneous reactions that accelerate secondary aerosol formation. Cyclone movement, with strong advection and updrafts, enables cross-ocean long-distance transport and upper troposphere injection of aerosols in the marine boundary layer, altering their global distribution. Wet deposition (rainout and washout) is the dominant removal mechanism, eliminating aerosols and mediating the cyclone–aerosol–cloud feedback loop, where aerosols as cloud condensation nuclei or ice nuclei regulate cyclone intensity, precipitation, and cloud cover. Current challenges (e.g., emission quantification uncertainties, incomplete microphysical understanding, model limitations) and prospects (e.g., enhanced long-term observations, improved model parameterization) are discussed. This review provides a scientific basis for aerosol-climate effect studies under extreme weather and references for related fields. Full article

The Caspian Sea’s unique environment and intense hydrocarbon extraction make it a high-risk, understudied region for oil spill modelling. This review assesses the physical, chemical, and biological processes governing oil spill transport and fate, and evaluates the principal numerical tools available for the Caspian Sea context. The weathering processes are reviewed from foundational formulations to operational implementations. Key research challenges identified include the absence of photo-oxidation from operational models, limited laboratory data for Caspian crude oil types, and simplified biodegradation parameterizations. Hydrodynamic forcing uncertainty, arising from the lack of a dedicated operational ocean model, remains the dominant source of trajectory forecast error. Seven operational oil spill modelling tools and the ROMS hydrodynamic platform are reviewed. Only OSCAR and MIKE 21 have documented applications to the Caspian Sea, representing a significant regional gap. ROMS is identified as the most suitable hydrodynamic platform for future operational forecasting. Finally, the integration of machine learning and deep learning methods, including neural network trajectory prediction and SAR detection, is discussed as a promising frontier for improving forecast accuracy in this data-sparse environment. Full article

Marine cold-air outbreaks (MCAOs) enhance air–sea exchange and are accompanied by substantial cloud-population reorganization over the North Atlantic. Previous satellite studies have documented MCAO-related cloud properties and International Satellite Cloud Climatology Project (ISCCP) weather-state distributions, but the continuous redistribution of the complete cloud population and its dependence on sampling scale remain less well quantified. We combine the merged H-series ISCCP (ISCCP-H) weather-state product with the ERA5 reanalysis over the North Atlantic sector of 35–50°N, 55–20°W during January–March and November–December of 2000–2009. Three complementary analytical strategies are used to examine scale-dependent cloud associations: intensity-binned analysis of positive-MCAO native ERA5 grid-point-days, domain-daily regressions, and ISCCP-H cell fixed-effect models. Across 1459 MCAO dates and 256,518 strict-ocean ISCCP-H cell-days, the combined frequency of shallow-cumulus-like and stratocumulus-like low-cloud weather states decreases systematically with MCAO intensity, with a domain-daily slope of −0.0562 K⁻¹. Compensating regional increases occur mainly in the midlatitude-storm and middle-to-high-cloud weather states, while the deep convective and anvil state shows no robust increase. After cell and calendar-month climatological differences are removed, the low-cloud reduction persists, together with positive associations for the midlatitude-storm and optically thick middle-top weather states. Cirrus is positively associated with MCAO intensity at the regional scale but negatively associated within fixed cells, consistent with geographical composition contributing to its regional response. MCAO intensity is therefore more consistently associated with total low-cloud weather-state occupancy than with the internal composition of the low-cloud subset. The closed weather-state framework provides an observational benchmark for satellite and climate model evaluation. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Data imputation has attracted considerable interest due to the importance of data quality, a key challenge in data science. Various statistical methods, and more recently machine learning techniques, have been developed to address the issue of missing values. In this study, we present an imputation method that integrates forecasting and backcasting using Long-Short-Term Memory (LSTM) architecture for predicting blocks of consecutive missing values. The proposed method was evaluated on a randomly generated absent group of data from a weather dataset. In this context, we assessed different hyperparameters using regression metrics. Initially, we trained and tested the models with varying data and sequence sizes on distinct units of missing data, subsequently applying the method to other units with specific data and sequence sizes. Additionally, we substituted the LSTM model with other machine learning algorithms applying, the same method, and we compared the results. Finally, we tested the method on missing blocks from a dataset obtained from the Digital Ocean Lab (DOL) weather station. Our findings indicate that this method effectively provides a reasonable estimation of missing values in time series datasets. Full article

This study presents a multivariate spatio-temporal clustering framework to characterise joint wind–wave regimes across European seas using the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; 1979–2014). Seasonal and annual statistics of significant wave height, mean wave period, wind speed, and wave/wind direction were computed at 0.5° resolution. Principal component analysis was used to reduce dimensionality, retaining 30 components that captured 99% of the variance. K-means clustering was then used to identify nine coherent dynamical regimes with persistent spatio-temporal signatures. These regimes were grouped into open-ocean, transitional, and enclosed/semi-enclosed categories based on internal variability, directional spread, and geographic exposure. Open-Atlantic regimes are found to be energy-rich, exhibiting clear December–February maxima in significant wave height ($H_s$), mean wave period ($T_{02}$), and 10 m wind speed ($W_{s10}$); enclosed and semi-enclosed basins show lower amplitudes and reduced variability, while transitional shelves and the southern Mediterranean display intermediate conditions, characterised by moderate $T_{02}$ levels and seasonal rotation of wave and wind directions, reflecting a mixed influence of locally generated seas and remotely forced swell. Dispersion analysis highlights a clear Atlantic–Mediterranean partition, with transitional shelves forming a dynamical bridge between open-ocean and enclosed basins. Teleconnection analysis shows that the North Atlantic Oscillation and Arctic Oscillation dominate Atlantic regimes, while the Scandinavia, East Atlantic, and Polar/Eurasia patterns modulate variability and directional persistence in transitional and enclosed seas. The classification defines a climatological framework of European wind–wave conditions and establishes a practical basis for renewable energy assessment, engineering design, and long-term change analysis, with methods transferable to other basins. Full article

Harnessing the powerful learning and modeling capabilities of artificial intelligence, this study introduces a deep learning-driven wind data quality control algorithm that employs correlation verification rules. By constructing a Dual-Track Information Fusion Network (DTF-Net), it captures local temporal variations in wind speed via the temporal track and uncovers physical coupling relationships among temperature, pressure, wind direction, and other variables through the global track. Integrating dynamic three-standard-deviation spike detection with 3δ-RMSE spatial validation based on deep learning predictions, the algorithm enables multi-dimensional collaborative anomaly detection in the absence of neighboring stations. Experimental findings demonstrate that the proposed method achieves Mean Absolute Errors (MAE) of 0.217, 0.398, and 0.462 for 1 h, 12 h, and 24 h wind speed forecasts, respectively, representing a 3.8–61.3% reduction compared to general-purpose models like AutoFormer, ITransformer, and FiLM. The anomaly detection rate for quality control ranges from 0.33% to 9.20%, effectively identifying data aberrations during buoy maintenance, equipment failures, and abrupt changes in short-term weather patterns. This study leverages the powerful learning and modeling capabilities of artificial intelligence to establish a novel and easily understandable intelligent quality-control paradigm for sparse ocean observation networks, providing direct practical value for improving the quality of marine meteorological data assimilation. Full article

Numerical weather simulations and forecasts are highly sensitive to environmental conditions. This is especially true in Taiwan, an ocean-surrounded island, during its transition season. Atmospheric temperature and moisture profiles retrieved from spaceborne sounders provide essential environmental information in regions lacking in situ observations. However, infrared sounders are sensitive to clouds and may induce uncertainties related to cloud properties. The present study analyzed 1 year of soundings from the National Oceanic and Atmospheric Administration’s Unique Combined Atmospheric Processing System (NUCAPS) to investigate the effects of clouds on the retrievals. The results indicated that the retrieved temperature profiles over land and under clouds had greater uncertainty than over oceans or in clear skies. In addition, the moisture profiles often exhibited a bias against cloud-top pressure. Therefore, this study proposed an objective quality control and bias correction method based on cloud effects. Excluding temperature observations affected by clouds and those over land reduced the root mean square difference from 3.3 K to 1.3 K. The relative cloud-top pressure level was used to conduct water vapor bias correction, which achieved effective correction for dry bias in the retrieved moisture profiles. After appropriate constraint criteria were applied, the bias-corrected profiles demonstrated a reduction in moisture bias from −4% to nearly 0%. That is, we assimilated sounding and radiance data into the regional Weather Research and Forecasting model and evaluated their effects, and we discovered that the retrieved profiles and direct observations positively contributed to the forecast of a spring frontal system. However, experiments using objective-bias-corrected sounding data improved skill scores in precipitation forecasts compared with using original sounding data or radiance data under a standard global operational baseline bias correction. Full article

Ship detection in complex maritime and remote sensing scenes is of great importance for Earth observation, maritime surveillance, and intelligent ocean monitoring. However, existing methods often struggle with severe background interference, adverse weather conditions, and insufficient structural feature representation of ship targets. To address these challenges, we propose VSTF-Net, a Vision-Semantic and Target-aware Fusion Framework for Ship Detection in Complex Maritime Sensing Scenarios. Specifically, to enhance high-level semantic representation under complex maritime environments, a Visual-Semantic Environment Modulation (VSEM) module is designed to introduce semantic priors extracted from a CLIP-pretrained visual encoder for adaptive feature modulation. To capture the elongated structural and directional characteristics commonly exhibited by ship targets in maritime and remote sensing scenes, a Multi-branch Asymmetric Dilated (MAD) module is developed to strengthen structural and directional feature modeling through asymmetric and dilated convolutions. Furthermore, to suppress high-frequency background interference in complex water environments, a Frequency-Adaptive Denoising (FAD) module is integrated to adaptively recalibrate frequency-domain features and improve the discriminability between ship targets and surrounding backgrounds. Experimental results show that the proposed method achieves 89.2% mAP@0.5 and 56.2% mAP@0.5:0.95 on the enhanced SeaShips dataset, with improvements of 1.9% and 2.4% over the baseline model, respectively. Further experiments on the HRSC2016 and MEIWVD datasets demonstrate the effectiveness and robustness of the proposed method across both remote sensing and maritime scenes. Full article

Ship weather routing in regional seas under severe seasonal weather systems, such as typhoons, poses a critical operational challenge for maritime safety and efficiency. Traditional single-agent reinforcement learning (RL) methods frequently suffer from training instabilities and erratic trajectory adjustments when exposed to the highly non-stationary, multi-scale dynamics of coupled wind–wave–current fields. To address these limitations, this study proposes a dual-agent hierarchical reinforcement learning framework (HRL-MOO) featuring a built-in dynamic risk-weight adaptation mechanism. The proposed global agent discerns large-scale environmental evolutions and adaptively updates the relative weights of wind-, wave-, and current-induced risks at a strategic level, while the local agent translates this macro-level guidance into short-term tactical heading and speed adjustments within realistic vessel motion boundaries. The framework incorporates bathymetric constraints through a high-resolution navigable domain mask derived from ETOPO topography to guarantee practical navigability. Simulated experiments are executed using hourly environmental reanalysis data and best-track records corresponding to the passage of Typhoon Yagi (2024) through the northern South China Sea and Taiwan Strait. The empirical results demonstrate that the cooperative dual-agent structure establishes a superior global Pareto frontier compared to conventional standard single-agent Proximal Policy Optimization (PPO) and metaheuristic baselines. Under extreme typhoon conditions, the HRL-MOO model effectively decreases the cumulative environment-induced risk from approximately 0.17 to 0.12, improves path smoothness by achieving a higher index of 0.842, and accelerates policy convergence to within 3000 training episodes, all while maintaining a highly efficient voyage distance with minimal detour overhead. Ablation studies further validate that the synergy between macro-level stream field recognition and adaptive multi-objective optimization significantly enhances decision-making stability. This framework offers a robust and interpretable computational tool for autonomous ship weather routing in fast-changing, high-risk ocean environments. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the $S_4$ index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive $S_4$ pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring. Full article

Topographic and geomorphic information provides an essential basis for human development and utilization of natural resources, disaster prevention and mitigation, ecological environment protection, and scientific research. Among spaceborne remote sensing approaches, Synthetic Aperture Radar (SAR) stands out due to its ability to actively transmit and receive microwave signals, enabling high spatial coverage, all–weather, and all–day observation. With the rapid development of miniaturized satellite constellations, high–revisit and high–resolution SAR data have become more accessible, offering unprecedented opportunities for dynamic ocean observation. However, existing SAR–based bathymetry methods based on power–spectrum analysis are susceptible to sea–spike noise and 180° directional ambiguity, limiting their accuracy in shallow coastal waters. To address these limitations, a Cross–Spectrum–based Wave Ray Tracking bathymetry retrieval algorithm (CS–WRT) is developed using high–resolution imagery from mini–SAR constellations including HiSea–1 and Chaohu–1. The method incorporates cross–spectrum analysis into a localized wave ray tracking framework to effectively suppress sea–spike noise and accurately extract shallow–water wave vectors. Applied to six SAR images over the Taiwan Strait, CS–WRT consistently outperformed the power–spectrum approach in coastal environments. In the Jinjiang coastal region, comparison with Electronic Navigational Chart (ENC) data yielded a root mean square error of 2.22 m, a mean absolute percentage error of 7.06%, and a Pearson correlation coefficient of 0.84. Analysis of the shoaling slope parameter k further revealed that stronger wave shoaling effects correlate with improved retrieval accuracy, suggesting its potential as a diagnostic indicator of retrieval reliability. Full article