Hindcast Dataset Research Articles

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system

This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.

Wave spectral partitioning for the SWIM spectrometer based on the wave age and parameter optimization method

The paper proposes a wave spectral partitioning method for the surface waves investigation and monitoring (SWIM) spectrometer based on the wave age and parameter optimization (WA-PO) method. The wave age criterion is a way to identify waves as wind waves or swells, and parameter optimization methods enable the modeling of wave spectra. The combination of the two can use the spectrum models to fit the SWIM spectra based on clarifying whether the spectrum models belong to wind waves or swells, which achieves spectral partitioning. The WA-PO method consists of four steps: peaks region dividing, spectral peaks searching, objective function constructing, and model parameters optimizing. The results show that compared with the partition products provided by the SWIM, the partition parameters obtained from the WA-PO method are closer to those of the Integrated Ocean Waves for Geophysical and other Applications (IOWAGA) numerical hindcast dataset developed from the WAVEWATCH III (WW3). The WA-PO method can prevent over-partitioning of the wave spectra and detect wind-wave components hidden in the swell partition.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

A transformer-based method for correcting significant wave height numerical forecasting errors

Accurate significant wave height (SWH) forecasting is essential for various marine activities. While traditional numerical and mathematical-statistical methods have made progress, there is still room for improvement. This study introduces a novel transformer-based approach called the 2D-Geoformer to enhance SWH forecasting accuracy. The 2D-Geoformer combines the spatial distribution capturing capabilities of SWH numerical models with the ability of mathematical-statistical methods to identify intrinsic relationships among datasets. Using a comprehensive long time series of SWH numerical hindcast datasets as the numerical forecasting database and ERA5 reanalysis SWH datasets as the observational proxies database, with a focus on a 72-hour forecasting window, the 2D-Geoformer is designed. By training the potential connections between SWH numerical forecasting fields and forecasting errors, we can retrieve SWH forecasting errors for each numerical forecasting case. The corrected forecasting results can be obtained by subtracting the retrieved SWH forecasting errors from the original numerical forecasting fields. During long-term validation periods, this method consistently and effectively corrects numerical forecasting errors for almost every case, resulting in a significant reduction in root mean square error compared to the original numerical forecasting fields. Further analysis reveals that this method is particularly effective for numerical forecasting fields with higher errors compared to those with relatively smaller errors. This integrated approach represents a substantial advancement in SWH forecasting, with the potential to improve the accuracy of operational SWH forecasts. The 2D-Geoformer combines the strengths of numerical models and mathematical-statistical methods, enabling better capture of spatial distributions and intrinsic relationships in the data. The method's effectiveness in correcting numerical forecasting errors, particularly for cases with higher errors, highlights its potential for enhancing SWH forecasting accuracy in operational settings.

Added value of seasonal hindcasts to create UK hydrological drought storylines

Abstract. The UK has experienced recurring periods of hydrological droughts in the past, including the drought declared in summer 2022. Seasonal hindcasts, consisting of a large sample of plausible weather sequences, can be used to create drought storylines and add value to existing approaches to water resources planning. In this study, the drivers of winter rainfall in the Anglian region in England are investigated using the ECMWF SEAS5 hindcast dataset, which includes 2850 plausible winters across 25 ensemble members and 3 lead times. Four winter clusters are defined using the hindcast winters based on possible combinations of various atmospheric circulation indices (such as the North Atlantic Oscillation, NAO; East Atlantic, EA, pattern; and El Niño–Southern Oscillation). Using the 2022 drought as a case study, we demonstrate how storylines representing alternative ways the event could have unfolded can be used to explore plausible worst-case scenarios over winter 2022/23 and beyond. The winter clusters span a range of temperature and rainfall response in the study region and represent circulation storylines that could have happened over winter 2022/23. River flow and groundwater level simulations with the large sample of plausible hindcast winters show that drier-than-average winters characterised by predominantly NAO−/EA− and NAO+/EA− circulation patterns could have resulted in the continuation of the drought with a high likelihood of below-normal to low river flows across all selected catchments and boreholes by spring and summer 2023. Catchments in Norfolk were particularly vulnerable to a dry summer in 2023 as river flows were not estimated to recover to normal levels even with wet winters characterised predominantly by NAO−/EA+ and NAO+/EA+ circulation patterns, due to insufficient rainfall to overcome previous dry conditions and the slow response nature of groundwater-dominated catchments. Through this analysis, we aim to demonstrate the added value of this approach to create drought storylines during an ongoing event. Storylines constructed in this way supplement traditional weather forecasts and hydrological outlooks, in order to explore a wider range of plausible outcomes.

Metocean conditions at two Norwegian sites for development of offshore wind farms

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea’s most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles.

Evaluating the Performance of the North Pacific Victoria Mode as an ENSO Predictor Based on Multimodel Ensemble Hindcasts

Abstract The Victoria mode (VM), similar to the Pacific meridional mode (PMM), is forced by North Pacific Oscillation atmospheric variability. Both the boreal spring VM and PMM can trigger the onset of El Niño–Southern Oscillation (ENSO) events in the following winter. Previous studies have examined the precursor relationship between the PMM and ENSO based on a subset of models drawn from the North American Multimodel Ensemble (NMME) system. They suggested that the PMM can act as a precursor to El Niño events, whereas it fails to predict La Niña events. Utilizing the hindcasts of these models from NMME, this study further investigates the role of the VM as an ENSO predictor to examine the real usefulness of the VM for ENSO prediction. When compared with the PMM, the VM can predict both El Niño and La Niña events with some skill, showing that the VM seems to be a more reliable predictor of ENSO. We found that the unique role of the VM in ENSO prediction originates from the symmetric impact of the VM on ENSO events. The VM, as a basin-scale sea surface temperature (SST) pattern, combines the role of the SST over the subtropical northeastern Pacific that is similar to the PMM in initializing El Niño events with that of the SST over the western North Pacific that is different from PMM in initializing La Niña events, resulting in the symmetric effect of the VM on ENSO prediction. Thus, it is useful to consider VM variability as a reference for ENSO prediction. Significance Statement We aim to investigate whether the robust relationship between the boreal spring Victoria mode (VM) and the following winter ENSO in observations and climate models has any real predictive use, thus bridging the gap between theory and practical application. Our analyses based mainly on model hindcast datasets examine the forecasting skill of the VM for El Niño and La Niña events. We also reveal the symmetric impact of VM on ENSO through the analysis of observations, which explains the VM’s skill in predicting both El Niño and La Niña events. This study deepens our understanding of the effect of North Pacific SST variability on ENSO.

Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations

Abstract. We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.

Climate analysis of wave systems for multimodal sea states in the Mediterranean Sea

We present a new methodology for the detection of directional wave systems at a given location. Our approach leverages image segmentation algorithms to group integral spectral parameters in homogeneous clusters, represented by as many centroids associated to specific wave directions and capable to summarize the modes of the local wave climate. The method was tested in the Mediterranean Sea taking advantage of a long-term hindcast dataset, and highlighted well-known climatic patterns consistent to previous research in the area. By identifying the prevailing wave directions, the method allows to synthesize information useful for a plethora of ocean and coastal engineering applications. Among these, we quantify the frequency of occurrence of crossing seas in the Mediterranean Sea.

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

Subseasonal Ensemble Prediction of Flash Droughts over China

Abstract Flash droughts have been occurring frequently worldwide, which has a serious impact on food and water security. The rapid onset of flash droughts presents a challenge to the subseasonal forecast, but there is limited knowledge about their forecast skills due to the lack of appropriate identification and assessment procedures. Here, we investigate the forecast skill of flash droughts over China with lead times up to 3 weeks by using hindcast datasets from the Subseasonal-to-Seasonal Prediction (S2S) project. The flash droughts are identified by using weekly soil moisture percentiles from two S2S forecast models (ECMWF and NCEP). The comparison with reanalysis shows that ECMWF and NCEP forecast models underestimate flash drought occurrence by 5% and 19% for lead 1 week. The national mean hit rates for flash droughts are 0.22 and 0.16 for ECMWF and NCEP models for lead 1 week, and they can reach 0.29 and 0.18 over South China. The ensemble of the two models increases equitable threat score (ETS) from ECMWF and NCEP models by 8% and 40% for lead 1 week. In terms of probabilistic forecast, ECMWF has a higher Brier skill score than NCEP, especially over eastern China, which is consistent with higher temperature and precipitation forecast skill. The multimodel ensemble has the highest Brier skill score. This study suggests the importance of multimodel ensemble flash drought forecasting. Significance Statement Flash droughts have raised considerable concern, but whether they can be predicted at subseasonal time scales remains unclear. This study evaluates forecast skill of flash droughts over China based on ECMWF and NCEP hindcast data. Focusing on the historical flash drought events identified by the onset speed and duration, it is found that the ECMWF model outperformed the NCEP model with higher hit rates, lower false alarm ratios, and higher equitable threat scores, especially during the first week. However, less than 30% of the drought events can be captured in most regions by both models. An ensemble of the two models showed skill improvement against the ECMWF model for both deterministic and probabilistic forecasts.

Characterization and classification of wave storm events and wave climate on the Sea of Marmara

This study aims to conduct a wave climate study and provide a detailed analysis of wave storm events on the Sea of Marmara based on a 40-year (1979–2018) hindcast dataset. The accuracy of the used dataset was initially investigated in terms of both climatic and storm conditions using five-year (2013–2017) measurements of significant wave height (Hm0) at the Silivri buoy station. For the wave climate, the measurements’ 5-year mean and maximum Hm0 values were compared with SWAN hindcasts. To detect wave storm events from a time series, a critical value was determined by using the mean and standard deviation of the measurements. After that, the corresponding hindcasted storms with the measurements were detected. This analysis showed that the hindcasts are compatible with the measurements not only for climatic conditions but also for storm conditions. Monthly and annual variations of mean and maximum Hm0 were examined based on a 40-year long-term wave dataset and different return period Hm0 values based on annual maximums were determined at various locations. Wave storm events in the long-term analysis have been characterized and classified based on the storm energy density (Es) in five storm categories according to their energy. Temporal analysis of wave storm events shows that there has been an increase in the number and severity of storm events in recent years. The middle regions of the sea and the northeastern coasts of Marmara Island and Kapıdağ Peninsula are the most affected regions by extreme sea conditions.

Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S.

AbstractSubseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models.

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-h time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast

This paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of −0.14 m and −0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.

QuantifyingP-wave secondary microseisms events: a comparison of observed and modelled backprojection

SUMMARYSecondary microseisms are caused by nonlinear interactions between ocean waves of approximately equal wavelengths and opposite propagation directions. This seismic forcing is evaluated using ocean sea-state hindcast data and further modulated by the bathymetric effect. The numerical ocean model provides a global activity representation of the secondary microseisms, from which we isolate major events. We backprojected teleseismic P-wave propagation into the Earth's mantle to validate these events as effective seismic sources. The ocean model provides spectral amplitude information for modelling microseisms generated seismic wavefield. A comparison of the backprojection for P and PP phases from observed and synthetic microseisms forcing indicates high reliability in the ocean model, at least for major sources. A combination of P and PP phases detected across a global network of stations enables global ocean coverage. We improve backprojection images even further by introducing a two-step stacking for the P phase to address the problem of unbalanced station distribution. Thresholds of microseisms events forces valuable for seismic imaging are determined by comparing backprojections and ocean models for the years 2015 and 2020. Finally, we extracted a catalogue of microseisms events every 3-hr from 1994 to 2020 from the ocean hindcast data set. This catalogue is an intriguing resource for future applications of interferometric imaging at large scale.

Climatological diagnostics and subseasonal‐to‐seasonal predictions of Madden–Julian Oscillation events

AbstractThe Madden–Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability, which serves as a primary source of subseasonal‐to‐seasonal (S2S) predictability. Noticeably, MJO is not always a regularly recurring cycle but is characterized by discrete episodes. In this study, considering the quasi‐consecutive actives and eastward propagating features, a standard metric is proposed to identify MJO events based on the real‐time multivariate MJO (RMM) index. The re‐identification of historical MJO events reveals that there were 5.4 MJO events each year since 1981, and the average duration of each event is about 31.5 days. More MJO events tend to occur in the boreal winter and spring, with stronger intensity, longer duration and faster propagating speed than those in the boreal summer. Furthermore, MJO events are more likely to initiate in Phases 2 and 5, with a longer lifetime than those initiating in other phases. The amplitude and propagation characteristics of MJO events are strongly modulated by the dominant modes of sea surface temperature interannual variability with a strong regional dependence. Based on hindcast datasets of six S2S models, the prediction skill for the MJO is evaluated in the perspective of individual events. The longest leading time of the skilful prediction for individual MJO events ranges from 11 to 17 days, far below the traditional recognition. This result could be attributed to the apparent prediction barrier of MJO initiation, that is, a rapid decrease in prediction skill when predictions are carried out before the initiation of MJO events. In addition, the prediction skills of MJO events depends on interannual variabilities, with relatively higher skills under the conditions of the El Niño and Indian Ocean basin warming. These findings may shed light on the complexity and challenges of profoundly understanding and skilfully predicting MJO events.

A general framework to obtain seamless seasonal–directional extreme individual wave heights—Showcase Ekofisk

Extreme wave climate provides the basis for safe design of offshore structures and is crucial for planning and executing offshore operations. Covariate modeling of extremes significantly enriches and improves estimates of return levels and exceedance probabilities of extreme sea states. Based on novel observational and hindcast datasets, we formulate a seasonal–directional extreme value model for individual wave heights and present exceedance probabilities for the Ekofisk oil and gas field, a location in the Central North Sea. Subsequently, we elucidate how to downscale estimates of monthly exceedance probabilities and return levels to daily maxima and illustrate how to retrieve consistent results on seamless directional sectors and different seasons, or for the entire covariate space. We conclude with a versatile statistical framework to obtain seasonal–directional extreme waves and reveal a strong seasonal and directional dependence of extreme individual waves at Ekofisk with the highest waves coming from northwest during winter. Moreover, we apply our approach to normalized wave heights and show noticeable variability depending on season and direction with implications for offshore design.

Hindcast based global wave statistics

A database of wave scatter-diagrams, describing the joint probability of wave height and wave period worldwide, has been developed. The goal of this work is to provide a practical and representative wave input for the design of structures subjected to waves. This new database is believed to improve significantly over existing alternatives. The paper presents the background of this new dataset. There are three main building blocks. First, an accurate wave data source is essential. We have used a state of the art numerical hindcast dataset here, with extensive calibration and validation by altimeters (IOWAGA dataset, from IFREMER). Second, a database of ship positions has been used to account for the bad weather avoidance. The effect of such routing on statistics encountered by sailing ships is here clearly demonstrated. Third, a flexible statistical model is introduced to fit the raw data. The statistical model aims to smooth out sampling uncertainties. The joint distribution is described by a conditional model, the marginal Hs distribution being modelled by a hybrid distribution, and the conditional period by a split generalized normal law. Eventually, the new scatter-diagrams dataset is compared to one existing alternative, and its limitations are discussed. The final result of this work is a set of wave height and period scatter-diagrams; these are given in the current paper.