Search Results (276)

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

The Copernicus European Regional Reanalysis (CERRA) was released in August 2022, providing a continental atmospheric reanalysis, and, in addition, the New European Wind Atlas (NEWA) is a recently released hindcast product that can be used to create a high temporal and spatial resolution wind resource atlas of Europe. In order to demonstrate the suitability of the NEWA and CERRA wind datasets for offshore wind energy applications, the accuracy of these datasets was assessed for the Mediterranean Sea, a basin with a high potential for the development of offshore wind projects. Long-term in situ measurements from 13 offshore locations along the basin were used in order to assess the performance of the CERRA and NEWA wind speed datasets in the hourly and seasonal time scales by using a variety of different evaluation tools. The results revealed that the CERRA dataset outperforms NEWA and is a reliable source for offshore wind energy assessment studies in the examined areas, although special attention should be paid to extreme value analysis of the wind speed. Full article

This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions. Full article

Accurate prediction of wind speed is critical for wind power generation and bias correction serves as an effective tool to enhance the precision of climate model forecasts. This study evaluates the effectiveness of three bias correction methods—Quantile Regression at the 50th percentile (QR50), Linear Regression (LR), and Optimal Threat Score (OTS)—for improving wind speed predictions at a height of 70 m from the NCEP CFSv2 model in Shanxi Province, China. Using observational data from nine wind towers (2021–2024) and corresponding model hindcasts, we analyze systematic biases across lead times of 1–45 days. Results reveal persistent model errors: overestimation of low wind speeds (<6 m/s) and underestimation of high wind speeds (>6 m/s), with the Root Mean Square Error (RMSE) exceeding 1.5 m/s across all lead times. Among the correction methods, QR50 demonstrates the most robust performance, reducing the mean RMSE by 11% in October 2023 and 10% in February 2024. Correction efficacy improves significantly at longer lead times (>10 days) and under high RMSE conditions. These findings underscore the value of regression-based approaches in complex terrain while emphasizing the need for dynamic adjustments during extreme wind events. Full article

Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy H_m0 measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme H_m0 underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. Full article

The seismicity levels in Oklahoma and southern Kansas have increased dramatically over the last 15 years. Past studies have identified the massive disposal of wastewater co-produced during oil and gas extraction as the driving force behind some earthquake clusters, with a small number of events directly linked to hydraulic fracturing (HF) stimulations. The present investigation is the first one to examine the role both of these activities played throughout the two states, under the same framework. Our findings confirm that wastewater disposal is the main causal factor, while also identifying several previously undocumented clusters of seismicity that were triggered by HF. We were able to identify areas where both causal factors spatially coincide, even though they act at distinct depth intervals. Overall, oil and gas operations are probabilistically linked at high confidence levels with more than 7000 felt earthquakes (M ≥ 2.5), including 46 events with M ≥ 4.0 and 4 events with M ≥ 5. Our analysis utilized newly compiled regional earthquake catalogs and established physics-based principles. It first hindcasts the seismicity rates after 2012 on a spatial grid using either real or randomized HF and wastewater data as the input, and then compares them against the null hypothesis of purely tectonic loading. In the end, each block is assigned a p-value, reflecting the statistical confidence in its causal association with either HF stimulations or wastewater disposal. Full article

The performance of monthly prediction of extreme precipitation from the BCC-CPSv3-S2Sv2 model over the Yellow River Basin (YRB) using historical hindcast data from 2008 to 2022 was evaluated in this study, mainly from three aspects: overall performance in predicting daily precipitation rates, systematic biases, and monthly prediction of extreme precipitation metrics. The results showed that the BCC-CPSv3-S2Sv2 model demonstrates approximately 10-day predictive skill for summer daily precipitation over the YRB. Relatively higher skill regions concentrate in the central basin, while skill degradation proves more pronounced in downstream areas compared to the upper basin. After correcting model systematic biases, prediction skills for total precipitation-related metrics significantly surpass those of extreme precipitation indices, and metrics related to precipitation amounts demonstrate relatively higher skill compared to those associated with precipitation days. Total precipitation (TP) and rainy days (RD) exhibit comparable skills in June and July, with August showing weaker performance. Nevertheless, basin-wide predictions within 10-day lead times remain practically valuable for most regions. Prediction skills for extreme precipitation amounts and extreme precipitation days share similar spatiotemporal patterns, with high-skill regions shifting progressively south-to-north from June to August. Significant skills for June–July are constrained within 10-day leads, while August skills rarely exceed 1 week. Further analysis reveals that the predictive capability of the model predominantly originates from normal or below-normal precipitation years, whereas the accurate forecasting of extremely wet years remains a critical challenge, which highlights limitations in capturing mechanisms governing exceptional precipitation events. Full article

The northern Portuguese coast has been increasingly subjected to wave-induced coastal flooding, highlighting a critical need for comprehensive overwash assessment in the region. This study systematically evaluates the total water levels (TWLs) and swash regimes over a 120 km stretch of the northern coast of Portugal. Traditional approaches to overwash assessment often rely on detailed models and location-specific data, which can be resource-intensive. The presented methodology addresses these limitations by offering a pragmatic balance between accuracy and practicality, suitable for extended coastal areas with reduced human and computational resources. A coastal digital terrain model was used to extract essential geomorphological features, including the dune toe, dune crest, and/or crown of defense structures, as well as the sub-aerial beach profile. These features help establish a critical threshold for flooding, alongside assessments of beach slope and other relevant parameters. Additionally, a wave climate derived from a SWAN regional model was integrated, providing a comprehensive time-series hindcast of sea-states from 1979 to 2023. The wave contribution to TWL was considered by using the wave runup, which was calculated using different empirical formulas based on SWAN’s outputs. Astronomical tides and meteorological surge—the latter reconstructed using a long short-term memory (LSTM) neural network—were also integrated to form the TWL. This integration of geomorphological and oceanographic data allows for a straightforward evaluation of swash regimes and consequently overwash potential. The accuracy of various empirical predictors for wave runup, a primary hydrodynamic factor in overwash processes, was assessed. Several reports from hazardous events along this stretch were used as validation for this method. This study further delineates levels of flooding hazard—ranging from swash and collision to overwash at multiple representative profiles along the coast. This regional-scale assessment contributes to a deeper understanding of coastal flooding dynamics and supports the development of targeted, effective coastal management strategies for the northern Portuguese coast. Full article

The thickness of the Arctic sea ice constitutes one of the crucial indicators of global climate change, and while deep learning has shown promise in predicting sea ice thickness (SIT), the field continues to grapple with the challenge of limited data availability. In this study, we introduce a Wasserstein Generative Adversarial Network–Long Short-Term Memory (WGAN-LSTM) model, which leverages the data generation capabilities of WGAN and the temporal prediction strengths of LSTM to perform single-step SIT prediction. During model training, the mean square error (MSE) and a novel comprehensive index, the Distance between Indices of Simulation and Observation (DISO), are used as two metrics of the loss function to compare. To thoroughly assess the model’s performance, we integrate the WGAN-LSTM model with the Monte Carlo (MC) dropout uncertainty estimation method, thereby validating the model’s enhanced generalization capabilities. Experimental results demonstrate that the WGAN-LSTM model, utilizing MSE and DISO as loss functions, improves comprehensive performance by 51.9% and 75.2%, respectively, compared to the traditional LSTM model. Furthermore, the MC estimates of the WGAN-LSTM model align with the distribution of actual observations. These findings indicate that the WGAN-LSTM model effectively captures nonlinear changes and surpasses the traditional LSTM model in prediction accuracy. The demonstrated effectiveness and reliability of the WGAN-LSTM model significantly advance short-term SIT prediction research in the Arctic region, particularly under conditions of data scarcity. Additionally, this model offers an innovative approach for identifying other physical features in the sea ice field based on sparse data. Full article

This paper describes and implements a time-spatial correction of the regional prediction wave system, compared with altimeter data, by using an ensemble Kalman filter. The technique is successful in areas with substantial wave height around the Azores islands. Using winds from ERA5 and wave spectral boundary conditions from a prior study, the SWAN wave model generates wave conditions in the Azores area for 6 years. The time-spatial correction model is determined by comparing the hindcast data with the data from seven altimetry satellites: ERS-1, ERS-2, ENVISAT, TOPEX/POSEIDON, Jason-1, Jason 2, and GEOSAT Follow ON. The hindcast results are then corrected with the correction model. Furthermore, in situ buoy measurements are then employed to validate the corrected hindcast data. The outcomes demonstrate a significant improvement in the wave predictions. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

During its service life, a deep-sea floating structure is likely to encounter extreme marine disasters. The combined action of wind and wave loads poses a threat to its structural safety. In this study, elliptical copula, Archimedean copula, and vine copula models are employed to depict the intricate dependence structure between wind and waves in a specific sea area of the Shandong Peninsula. Moreover, hourly significant wave height, spectral peak period, and 10 m average wind speed hindcast data from 2004 to 2023 are utilized to explore the joint distribution of multidimensional parameters and environmental design values. The results indicate the following: (1) There exists a significant correlation between wind speed and wave parameters. Among them, the C-vine copula model represents the optimal trivariate joint distribution, followed by the Gaussian copula, while the Frank copula exhibits the poorest fit. (2) Compared with the high-dimensional symmetric copula models, the vine copula model has distinct advantages in describing the dependence structure among several variables. The wave height and period demonstrate upper tail dependence characteristics and follow the Gumbel copula distribution. The optimal joint distribution of wave height and wind speed is the t copula distribution. (3) The identification of extreme environmental parameters based on the joint probability distribution derived from environmental contour lines is more in line with the actual sea conditions. Compared with the design values of independent variables with target return periods, it can significantly reduce engineering costs. In conclusion, the vine copula model can accurately identify the complex dependency characteristics among marine variables, offering scientific support for the reliability-based design of floating structures. Full article

Typhoons cause significant losses and pose substantial threats every year, with an increasing trend observed in recent years. This study evaluates significant wave height (SWH) hindcasts for typhoons affecting Taiwan using optimized wind field configurations within the SCHISM-WWM-III coupled model. To enhance typhoon-induced SWH simulations, the blended wind field integrates ERA5 reanalysis wind data with the modified Rankine vortex wind model. Key parameters, including the parametric wind field start time, best track data, and the radius of maximum wind speed, were carefully selected based on analyses of typhoons Meranti and Megi in 2016. Validation metrics such as the skill core, HH indicator, maximum SWH difference, and peak time difference of the SWH indicate that the optimized setup improves the accuracy of simulation. The findings highlight the effectiveness of the adjusted blended wind field, the high-resolution best track data provided by Taiwan, and the maximum wind speed radius in significantly enhancing the accuracy of typhoon wave modeling for the waters surrounding Taiwan. Full article

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance. Full article