Oceanographic Forecast Research Articles

Learning-based Pareto-optimum routing of ships incorporating uncertain meteorological and oceanographic forecasts

In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.

Operational Oceanography in Ports and Coastal Areas, Applications for the Management of Pollution Events

Maritime safety and the protection of the marine environment were the primary objectives of two European projects that the National Research Council of Italy had participated in, with numerical applications in two areas located in the northern part of Sardinia, Italy. Specifically, two operational Numerical Prediction Systems (NPS) for pollution risk management were developed; the first was applied to the area of the Bonifacio Strait and the Gulf of Asinara and the second to the port of Olbia. These systems are composed of many oceans and particle tracking numerical models. They are forced with meteorological and ocean data provided by the European Centre for Medium-Range Weather Forecasts and Copernicus Marine Service and their outputs have been compared with in situ measurements for preliminary calibration. A web graphical interface was ad hoc designed, specifically responding to projects’ needs, providing online access to a 3-day oceanographic forecast and advanced diagnostic variables like Oil Stranding Time, Risk Score and Water Age. These products, along with the interactive web platform, prove invaluable for marine spatial planning, prevention and emergency management at sea, for the use of competent governmental and local bodies.

Hardware accelerated ray-tracing in ocean acoustics

Due to the expense and complexity of real-world data collection, machine learning applications in ocean acoustics demand ever-faster and more realistic models for training. In this talk, I discuss some recent GPU technologies which support hardware accelerated ray tracing and how they can be exploited to boost the throughput of certain types of calculations. Firstly, I describe RTX-Prop, which is a large-scale range-dependent Gaussian beam model which benefits from hardware-acceleration of bounding volume hierarchy searches and geometry intersection calculations. Designed to ingest oceanographic forecasts from the Royal Australian Navy’s Bluelink system, RTX-Prop has been developed as an embedded model to support tactical decisions by autonomous underwater vehicles. In the second application, I discuss hardware acceleration in the context of high-frequency sonar simulation. By synthesising fine-scale environmental detail, we have previously shown that simulated imagery can be almost indestinguishable from real data. Simulated sidescan and SAS imagery from our S4 model has recently become the basis of a machine-learning pipeline for training detectors and classifiers for naval mine countermeasures. [Work supported by the Trusted Autonomous Systems Defence Coorperative Research Centre.]

Operational forecasts of wave-driven water levels and coastal hazards for US Gulf and Atlantic coasts

Predictions of total water levels, the elevation of combined tides, surge, and wave runup at the shoreline, are necessary to provide guidance on potential coastal erosion and flooding. Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700 km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5 km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. The modeling system also enables continued research into wave-driven processes at a broad range of coastal areas.

Modeling demersal fish and benthic invertebrate assemblages in support of marine conservation planning.

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community‐level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3–34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf‐Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community‐level modeling provides a biotic‐informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well‐connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.

Random-forest based adjusting method for wind forecast of WRF model

Nowadays, machine learning (ML) methods have gained much attention and have been applied in some important related applications in earth science field, including observation data mining, geoscience image recognition, remote sensing image classification and so on. These ML-based applications play important roles in our daily life. However, in meteorological and oceanographic forecast, numerical is still the most popular method. Although researchers have proposed some ML-based prediction methods to overcome the shortcomings of numerical weather forecast methods, the explainability for the forecast result of artificial intelligence (AI) technology is still not as good as numerical weather forecast methods. Therefore, in this paper, we propose a random forest based adjusting method, which introduces AI technology to correct wind prediction results of numerical model. The proposed adjusting method greatly improves the accuracy of forecast results. Furthermore, the physical meanings of parameters in the numerical model are retained in adjusting results. From experimental evaluations, it is obvious that the root mean square error (RMSE) of each feature is reduced greatly. In detail, the average RMSE of 10m wind decreased by more than 45%, and the average RMSE of sea level pressure decreased by more than 50%. It is worth noting that the improvement here is the average of all forecasts for whole region within 7 days.

Predictability of a short-term emergency assessment system of the marine environmental radioactivity

ABSTRACTA Short-Term Emergency Assessment system of the Marine Environmental Radioactivity (STEAMER) was developed at the Japan Atomic Energy Agency (JAEA) to forecast or reanalyze the oceanic dispersion of radionuclides that are released into the ocean around Japan from nuclear facilities during routine operation or in an emergency. STEAMER is currently in daily operation at JAEA to conduct single forecast simulation. The predictability of STEAMER is validated by utilizing oceanographic forecast and reanalysis data in this study. The oceanic dispersion simulations that use oceanographic reanalysis data as input data are assumed to have true solutions. Reanalysis data that has been optimized by data assimilation is the most reliable input for post-analysis. Rigorous oceanic dispersion simulations are conducted for the hypothetical release of Cs-137 from the Fukushima Daiichi Nuclear Power Plant. The predictability of the Cs-137 oceanic dispersion is quantitatively estimated over a forecast period. Moreover, ensemble forecast simulations are also performed applying the Lagged Average Forecast methodology and they successfully improve the predictability of the Cs-137 oceanic dispersion over that obtained using single forecast simulation. The ensemble forecast simulations need to be installed in STEAMER in the future.

Cross-scale operational oceanography in the Adriatic Sea

ABSTRACTThe oceanographic forecast capability in coastal seas is often limited by the capacity of the numerical models in correctly reproducing the complex morphology of the coastline and the exchange processes between the shelf and the open seas. In the marginal Adriatic Sea this task is of uppermost importance due to the presence of several coastal water bodies and rivers. We present here a new operational oceanographic system, called Tiresias, based on the unstructured grid model SHYFEM and representing the whole Adriatic Sea together with the lagoons of Marano-Grado, Venice and Po Delta. The novelty of this oceanographic system resides in the very high-resolution, up to 10 m, of the numerical mesh, and in the high spatial and temporal resolution of the forcing and boundary conditions that drive the forecasts. The forecast results are evaluated against sea temperature and salinity profiles, mean circulation fields derived from a regional ocean model, tide gauges and drifter trajectory. The presented results highlighted the capacity of Tiresias in forecasting the general circulation in the Adriatic Sea, as well as several relevant coastal dynamics, such as saltwater intrusion, storm surge and riverine waters dispersion.

Application of Data Mining Technology Based on Wireless Sensor Networks in Oceanographic Forecasting

This paper aims to present a desirable prediction method for oceanographic trends. Therefore, an online monitoring scheme was prepared to collect the accurate oceanographic hydrological data based on wireless sensor network (WSN) and computer technology. Then, the data collected by the WSN were processed by support vector regression algorithm. To obtain the most important parameters of the algorithm, the particle swarm optimization was introduced to search for the global optimal solution through the coopetition between the particles. After that, an oceanographic hydrological data collection and observation system was created based on the hydrological situation of New York harbour. Then, the traditional support vector regression and the proposed method were applied to predict the oceanographic trends based on water temperature, salinity and other indices. The results show that the proposed algorithm enhanced the data utilization rate of the WSN, and achieved good prediction accuracy. The research provides important insights into the application of advanced technology in oceanographic forecast.

Faktory, snizhayushchie effektivnost' raboty sistem operativnykh okeanograficheskikh prognozov v Arkticheskom basseyne

Faktory, snizhayushchie effektivnost' raboty sistem operativnykh okeanograficheskikh prognozov v Arkticheskom basseyne

Factors Reducing Efficiency of the Operational Oceanographic Forecast Systems in the Arctic Basin

Factors Reducing Efficiency of the Operational Oceanographic Forecast Systems in the Arctic Basin

DEVELOPMENT OF AN OPERATIONAL ELBE TIDAL ESTUARY MODEL

Due to local tidal conditions, vessels coming in from the North Sea and bound for the port of Hamburg - more than 100 km upstream - require continually updated hydrological and oceanographic forecasts. The principal objective of the research project OPTEL (Operational Elbe Tidal Estuary Model) is to provide hydrological and oceanographic forecasts about water levels, depths, currents, salinity, temperature and ice conditions for any tidally influenced place on the river Elbe. By using a numerical model, it is ensured that water levels and currents are coupled dynamically, which allows peak water levels, slack water times, and - in 3D modelling - flow profile to be modelled in a physically consistent way and with high resemblance to natural conditions. The model concept including model output statistics (MOS) and first model results are presented.

At-sea real-time coupled four-dimensional oceanographic and acoustic forecasts during Battlespace Preparation 2007

Systems capable of forecasting ocean properties and acoustic performance in the littoral ocean are becoming a useful capability for scientific and operational exercises. The coupling of a data-assimilative nested ocean modeling system with an acoustic propagation modeling system was carried out at sea for the first time, within the scope of Battlespace Preparation 2007 (BP07) that was part of Marine Rapid Environmental Assessment (MREA07) exercises. The littoral region for our studies was southeast of the island of Elba ( Italy) in the Tyrrhenian basin east of Corsica and Sardinia. During BP07, several vessels collected in situ ocean data, based in part on recommendations from oceanographic forecasts. The data were assimilated into a four-dimensional high-resolution ocean modeling system. Sound-speed forecasts were then used as inputs for bearing- and range-dependent acoustic propagation forecasts. Data analyses are carried out and the set-up of the coupled oceanographic–acoustic system as well as the results of its real-time use are described. A significant finding is that oceanographic variability can considerably influence acoustic propagation properties, including the probability of detection, even in this apparently quiet region around Elba. This strengthens the importance of coupling at-sea acoustic modeling to real-time ocean forecasting. Other findings include the challenges involved in downscaling basin-scale modeling systems to high-resolution littoral models, especially in the Mediterranean Sea. Due to natural changes, global human activities and present model resolutions, the assimilation of synoptic regional ocean data is recommended in the region.

Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data

The prediction of the acoustic field evolution on a day to week frame, in a given oceanic area, is an important issue in sonar performance modeling. It relies primarily on acoustic propagation models, which convert water column and geometric/geoacoustic parameters to ‘instantaneous’ acoustic field estimates. In practice, to model the acoustic field, even the most accurate acoustic models have to be fed with simplified environmental descriptions, due to computational issues and to a limited knowledge of the environment. This is a limitation, for example, in acoustic inversion methods, in which, by maximizing the proximity between measured and modeled acoustic signals, the estimated environmental parameters are deviated from reality, forming what is normally called an ‘acoustically equivalent environment’. This problem arises also in standard acoustic prediction, in which, the oceanographic forecasts and bottom data (typically from archives) are fed directly to an acoustic model. The claim in the present work is that, by converting the oceanographic prediction and the bottom properties to ‘acoustically equivalent’ counterparts, the acoustic prediction can be obtained in an optimal way, adapted to the environmental model at hand. Here, acoustic prediction is formulated as a Bayesian estimation problem, in which, the observables are oceanographic forecasts, a set of known bottom parameters, a set of acoustic data, and a set of water column data. The predictive posterior PDF of the future acoustic signal is written as a function of elementary PDF functions relating these observables and ‘acoustically equivalent’ environmental parameters. The latter are obtained by inversion of acoustic data. The concept is tested on simulated data based on water column measurements and forecasts for the MREA'03 sea trial.

Performance of operational systems with respect to water level forecasts in the Gulf of Finland

This paper is devoted to the validation of water level forecasts in the Gulf of Finland. Daily forecasts produced by four setups of operational, three-dimensional Baltic Sea oceanographic models are analyzed using statistical means and are compared with water level observations at three Finnish stations located on the northern coast of the Gulf of Finland. The overall conclusion is that the operational systems were skillful in forecasting water level variations during the study period from November 1, 2003, to January 31, 2005. The factors causing differences between the water level forecasts of different models are discussed as well. An important task of operational sea level forecasting services is to provide accurate and early information about extreme water levels, both positive and negative surges. During the study period, two major winter storms occurred which caused coastal flooding in the region. According to our analysis, the operational models forecast the rise of water levels during these events rather successfully. Nowadays, operational forecasts can provide early warnings of extreme water levels at least 1 day in advance, which may be regarded as a minimum requirement for an operational forecasting system. The paper concludes that the models generally performed very well, with over 93% of the hourly water level forecasts found to be within the range of ±15 cm of the observed water levels, and with the timing of the water level peaks accurately predicted. Further discussion and studies dealing with the assessment of the skills of both operational meteorological and oceanographic forecasts, especially in connection with rare surge events, will be necessary. Skill assessment of operational oceanographic models would be relatively easy if acceptable error limits or a quality system was developed for the Baltic Sea operational models.

An Operational European Global Ocean Observing System for the Eastern Mediterranean Levantine Basin: The Cyprus Coastal Ocean Forecasting and Observing System

The countries surrounding the Mediterranean Sea have joined together in several multinational initiatives to conduct long-term, integrated, operational oceanographic observations and modelling of this important region. Some of these initiatives and the country members involved are discussed in this paper. Particular emphasis is given to long-term observing systems and modelling conducted in the Eastern Mediterranean Levantine Basin and the region around the island of Cyprus. A complete operational oceanographic forecasting and observing system has been developed in Cyprus, and has been operational since early 2002. The system is called CYCOFOS—Cyprus Coastal Ocean Forecasting and Observing System—and is a component of the Global Ocean Observing System (GOOS), and its European (EuroGOOS) and Mediterranean (MedGOOS) modules. CYCOFOS is the result of several years of research activities all carried out within the framework of European Union-funded projects including: (1) Mediterranean forecasting system, both pilot project and towards environmental predictions (MFSPP and MFSTEP), (2) Mediterranean network to Access and upgrade Monitoring and forecasts Activities in the region (MAMA), (3) European Sea level Service Research Infrastructure (ESEAS-RI), (4) Mediterranean network of Global sea Level Observing System (MedGLOSS), and (5) Marine Environment and Security in the European Areas (MERSEA strand 1). CYCOFOS at present consists of several operational modules, including flow and offshore waves forecasts, satellite remote sensing, coastal monitoring stations and end user-derived applications. All these operational modules provide regular near-real-time information, both to local and sub-regional end users in the Eastern Mediterranean Levantine Basin. This paper discusses these as well as additional ocean observation stations and features soon to be added to CYCOFOS.

High resolution and efficient oceanographic and acoustic modeling of propagation through mesoscale oceanic features

A procedure is presented for combining the output of advanced multilevel “primitive equation” dynamic oceanographic forecast models with high fidelity underwater acoustic propagation models. This procedure is appropriate for the development of realistic predictions of acoustic transmissions through complicated mesoscale features, such as currents, rings, eddies, and fronts. Examples of oceanographic predictions using the Princeton dynamic ocean model developed by Mellor and Blumberg are displayed graphically for a dataset of the Gulf of Mexico. Results pertinent to data interpolation, the identification of mesoscale oceanic features, and 3-D visualization are presented. Ocean data are converted to sound-speed profiles for this region and are interfaced to a range-dependent parabolic equation (PE) propagation loss model. As a low-frequency acoustic source is moved successively from shallow to deep water in the Gulf of Mexico, results are given that illustrate large variations in propagation loss to a receiver located in deep water. The relative contributions of bathymetry and mesoscale oceanic features to these variations are analyzed. Finally, a scheme for speeding up PE calculations by utilizing the massively parallel computational capabilities of the Connection Machine at GE/ATL is discussed.

A Review of the Program of the Ocean Products Center

Abstract The Ocean Products Center (OPC) represents a consolidation of personnel from different parts of the National Oceanic and Atmospheric Administration (NOAA). The purpose of OPC is to generate and disseminate various marine meteorological, and oceanographic analyses and forecasts. Emphasis is placed on using real-time data, analysis, and forecast fields from operational numerical weather prediction models to produce relevant products for marine applications. This article describes some of the operational products that are routinely disseminated to various users, and research activities for improving the quality of the operational products and the generation of new ones.

The effect of thermohaline steps on range‐dependent acoustic propagation near the northeast coast of South America using high resolution data

The effects of large thermohaline steps on acoustic transmission loss (TL) for a range‐independent sound velocity profile (SVP) near the northeast coast of South America were reported [Chin‐Bing and King, J. Acoust. Soc. Am. Suppl. 1 76, S84 (1984)] using a high resolution SVP (in depth), a discrete standard depth SVP, and a depth thinned SVP. The thermohaline step structure created adjacent regions of near isovelocity connected by a high gradient small transition region; this “step” structured SVP affected the TL even at low frequencies. The study is now extended to include recent experimental high resolution range‐dependent thermohaline data. Range‐dependent TL comparisons are made using SVPs obtained from (1) the high resolution data, (2) an oceanographic forecast model, (3) discrete standard depths, and (4) a depth thinned profile. This range‐dependent study supports the basic finding from the range‐independent study—TL structure can be adversely affected by failing to include the detailed SVP step structure. Furthermore, oceanographic forecasts models that provide SVPs need a high degree of accuracy in regions where these vertical anomalies exist.

Environmental Constraints on Drill Rig Configurations

Factors in determining an exploratory drilling vessel's efficient potential work capability for operations in the Eastern Seaboard coastal region are engineering oriented, predicated upon oceanographic and meteorological weather forecasting, seafloor composition, and order-of-magnitude vessel response amplitudes to sea conditions. Determined from vessel response information is the percentage of time exceedence for each motion when related to the drillfloor operational constraints of drilling, tripping, running casing, etc. The drillfloor activities are described as percentages of the total operation from which a cost-effective utilization factor is obtained or basically predicted vessel downtime. From an assigned utilization factor, the vessel's potential work capability is determined as a function of initial probable differences in capital costs and the comparative operational capabilities or limitations inherent in each vessel concept. The geometry of vessels reviewed covers a wide spectrum of twin-hull semisubmersibles, drillships, and bottom-supported multilegged self-elevating platforms.