Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Transformation of a wave energy spectrum from encounter to absolute domain when observing from an advancing ship

The article presents a practical approach to transform a wave energy spectrum from encounter domain to absolute domain. This problem has its specific relevance, when shipboard sea state estimation is conducted by the wave buoy analogy; notably for some particular implementation solving for the sea state directly in the encounter domain. In this context, the encounter domain is that observed from a ship when it advances in a seaway, whereas the absolute domain is that corresponding to making observations from a fixed point in the inertial frame. Spectrum transformation can be uniquely carried out if the ship sails “against” the waves (beam to head sea) but in following sea conditions there exists no unique solution to the problem. Instead, a reasonable approach valid for practical engineering must be applied, and the article outlines one viable solution that can be used to transform a wave spectrum from encounter to absolute domain. Specifically, two pseudo algorithms are presented, and good performance is achieved with both algorithms when they are tested at different operational scenarios.     

Use of advanced directional wave spectra analysis methods

Frequency-dependent cross-spectral parameters for pitch-roll buoy data and parameters that describe directional wave spectra based on a directional Fourier series are developed by the National Data Buoy Center (NDBC) and other organizations that collect wave data. The World Meteorological Organization (WMO) specifies wave data product formats in its Wave Observation (FM 65 WAVEOB) code. Other organizations, such as the US Army Corps of Engineers Field Wave Gaging Program (FWGP), have similar specifications. A directional Fourier series has poor directional resolution compared with advanced methods such as those based on maximum likelihood and maximum entropy. Transformations are developed for applying the advanced methods and working with directional wave information stored in the WMO's FM 65 WAVEOB code, the FWGP's Wave Data Analysis Standard (WDAS) format, and similar codes and formats. Using the transformations, a directional Fourier series expansion method, a Maximum Likelihood Method (MLM), and a Maximum Entropy Method (MEM) are each applied to 115 sets of NDBC directional wave data. The MEM and MLM provide better directional resolution, but the MEM produces artificial double peaks. There are considerable differences for the three used methods. The developed transformation equations enable wave data users to apply the method that best suits their applications.     

Forecasting and hindcasting waves with the SWAN model in the Southern California Bight

The Naval Research Laboratory created a wave forecasting system in support of the Nearshore Canyon Experiment (NCEX) field program. The outer nest of this prediction system encompassed the Southern California Bight. This forecasting system is described in this paper, with analysis of results via comparison to the extensive buoy network in the region. There are a number of potential errors, two of which are poor resolution of islands in the Bight—which have a strong impact on nearshore wave climate—and the use of the stationary assumption for computations. These two problems have straightforward solutions, but the solutions are computationally expensive, so an operational user must carefully consider their cost. The authors study the impact of these two types of error (relative to other errors, such as error in boundary forcing) using several hindcasts performed after the completion of NCEX. It is found that, with buoy observations as ground truth, the stationary assumption leads to a modest increase in root-mean-square error; this is due to relatively poor prediction of the timing of swell arrivals and local sea growth/decay. The model results are found to be sensitive to the resolution of islands; however, coarse resolution does not incur an appreciable penalty in terms of error statistics computed via comparison to buoy observations, suggesting that other errors dominate. Inaccuracy in representation of the local atmospheric forcing likely has a significant impact on wave model error. Perhaps most importantly, the accuracy of directional distribution of wave energy at the open ocean boundaries appears to be a critical limitation on the accuracy of the model-data comparisons inside the Bight.     

Real-time wave forecasting using genetic programming

The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24 h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.     

Measurement of ocean wave spectra using a ship-mounted HF radar

The work describes an inversion algorithm for HF radar measurement of nondirectional wave spectra using an omnidirectional receive/transmit antenna. Such a radar would be suitable for deployment on a stationary ship or drill rig. In this approach, wave information is extracted from the radar observations by numerically inverting the integral equation representing the backscatter return from the ocean. Test results of this technique applied to data collected using a 25.4-MHz radar installed on a ship have been very positive. For the two measurements collected, there is a high degree of correlation between the radar wave estimates and those of a WAVE-TRACK buoy     

The winds of the comparison data set for the Seasat Gulf of Alaska experiment

The ship and data buoy winds used for comparison in the validation of Seasat-derived winds are described in terms of the time series of hourly wind observations from the buoys and in terms of the technique used to produce 20- and 30-min average winds from the ships. Sources of scatter in the comparison data are briefly reviewed.     

Use of nautical radar as a wave monitoring instrument

Common marine X-Band radars can be used as a sensor to survey ocean wave fields. The wave field images provided by the radars are sampled and analysed by a wave monitoring system (called WaMoS II) developed by the German research institute GKSS. This measuring system can be mounted on a ship, on offshore stations or at coastal locations. The measurement is based on the backscatter of microwaves from the ocean surface, which is visible as ‘sea clutter' on the radar screen. From this observable sea clutter, a numerical analysis is carried out. The unambiguous directional wave spectrum, the surface currents and sea state parameters such as wave periods, wave lengths, and wave directions can be derived. To provide absolute wave heights, the response of the nautical radar must be calibrated. Similar to the wave height estimations for Synthetic Aperture Radars, the so-called ‘Signal to Noise Ratio' leads to the determination of the significant wave height (H_S). In this paper, WaMoS II results are compared with directional buoy data to show the capabilities of nautical microwave radars for sea state measurements.     

A note on nearshore wave features: Implications for wave generation

This paper analyses 10 years of wave data from the Mediterranean Spanish (Catalan) coast considering the mean wave climate and storm events from the standpoint of wind-wave momentum transfer and wave prediction. The data, registered by a buoy at about 12 km from the coastline, revealed two main groups of wave storms, with NW and E directions. NW storms correspond to a fetch-limited situation since the intense wind blows from land. Low-pressure centres located over the Mediterranean Sea produce easterly storms. Near the coast the eastern winds from the sea are replaced by NW winds coming from meteorological patterns over northern Spain and south-western France. Wave storms are classified and studied to obtain their main features (including spectral width, wave length, wave age and bimodality) and discussed in terms of wind-wave momentum transfer for operational wave predictions. Observations show a complex coastal wave climate. Fetch-limited storms presented smaller spectral widths while varying wind situations presented larger widths due to the presence of bimodal spectra. These wave features are highly relevant for wind–ocean momentum transfer and, thus, for current and wave predictions. The spectral width proved to be a good indicator of sea complexity and is thus applicable for improved wind drag estimations. A new drag coefficient formulation is proposed, based on existing wind dependent drag expressions, but including also spectral wave properties (a spectral width parameter) that highlights the characteristics of wind-wave generation under pre-existing swell. Such a formulation, once properly validated with field observations, is expected to improve wind-wave predictions.     

Analysis of the Voyager storm

We analyse the wind and wave conditions present in the Mediterranean Sea at the time and location when the cruise ship Voyager was reportedly hit by one or more big waves and suffered substantial damage. The analysis is done using wind and wave modelling supported by satellite and buoy wind and wave data. Granted the hindcast of the storm, we also analyse the local conditions for the possibility of freak waves.     

Testing and calibrating parametric wave transformation models on natural beaches

To provide coastal engineers and scientists with a detailed inter-comparison of widely used parametric wave transformation models, several models are tested and calibrated with extensive observations from six field experiments on barred and unbarred beaches. Using previously calibrated (“default”) values of a free parameter γ, all models predict the observations reasonably well (median root-mean-square wave height errors are between 10% and 20%) at all field sites. Model errors can be reduced by roughly 50% by tuning γ for each data record. No tuned or default model provides the best predictions for all data records or at all experiments. Tuned γ differ for the different models and experiments, but in all cases γ increases as the hyperbolic tangent of the deep-water wave height, H_o. Data from two experiments are used to estimate empirical, universal curves for γ based on H_o. Using the new parameterization, all models have similar accuracy, and usually show increased skill relative to using default γ.     

星载高级合成孔径雷达波模式算法及其反演数据精度验证

搭载在欧洲环境卫星(ENVISAT)上的高级合成孔径雷达(Advanced Synthetic Aperture Radar,ASAR)二级波模式数据提供了诸多海浪信息包括有效波高、波向、波长和二维海浪谱等,在海浪预报模式中具有重要作用。本文拟利用浮标观测数据对ASAR波模式算法及其反演数据精度进行对比验证。由于SAR卫星在海面的特殊成像机制,不同海况下会有不同的测量结果,通过与美国国家浮标中心(NDBC)的浮标数据对比,显示ASAR有效波高在高海况下低估和在低海况下高估的现象,在中等海况下的测量结果较优。通过研究ASAR数据集中对应的海浪谱,按照能量与方向分布可分为四种类型:单一方向海浪谱(Ⅰ类谱),180°方向模糊海浪谱(Ⅱ类谱),海浪两个方向且能量分布杂乱(Ⅲ类谱),多个传播方向且谱型杂乱海浪谱(Ⅳ类谱)。探究在不同类型下的海浪参数的精度,结果表明在单一波向正常海浪谱情况下,有效波高、波向与浮标数据一致性较好,存在180°方向模糊的对称海浪谱仅有效波高精度较高,谱型杂乱的海浪谱海浪有效波高和波向反演结果均较差。     

Wave prediction using wave rider position measurements and NARX network in wave energy conversion

Several control methods of wave energy converters (WECs) need prediction in the future of wave surface elevation. Prediction of wave surface elevation can be performed using measurements of surface elevation at a location ahead of the controlled WEC in the upcoming wave. Artificial neural network (ANN) is a robust data-learning tool, and is proposed in this study to predict the surface elevation at the WEC location using measurements of wave elevation at ahead located sensor (a wave rider buoy). The nonlinear autoregressive with exogenous input network (NARX NN) is utilized in this study as the prediction method. Simulations show promising results for predicting the wave surface elevation. Challenges of using real measurements data are also discussed in this paper.     

Latching control of deep water wave energy devices using an active reference

This paper investigates latching type control on a floating wave energy converter in deep water. An on-board, actively controlled motion-compensated platform is used as a reference (‘active reference’) for power absorption and latching. A variational formulation is used to evaluate an optimal control sequence in the time domain. Time domain simulation results are presented for a heaving buoy in small-amplitude waves. Results are compared with an equivalent system where latching and power absorption are from a sea-bottom-fixed reference.     

时空窗口对HY-2有效波高产品检验影响模拟研究

时空窗口的选择是卫星高度计有效波高产品检验的主要影响因素。采用Monte Carlo(MC)数学模拟的方法 ,研究了时空窗口对HY-2高度计有效波高检验的影响,并采用现场浮标测量数据验证了MC模拟的可靠性。MC模拟结果表明,采用浮标测量数据对HY-2高度计有效波高检验时,必须分海况选取对应的最优空间窗口进行,并给出不同海况下的最优的时空窗口。对于高海况需采用小的空间窗口,在1 m,2 m,3 m,4 m有效波高的海况下,其理想的时空窗口为0 min,117 km,30 km,18 km和13 km。     

Comparison of surface wind stress characteristics over the Tropical Atlantic (10°N–40°S) in fields derived from the UWM/COADS, NCEP/NCAR and QuikSCAT datasets

A comparison of monthly wind stress derived from winds of NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis and UWM/COADS (The University of Wisconsin-Milwaukee/Comprehensive Ocean-Atmosphere Data Set) dataset (1950–1993), and of NCEP/NCAR reanalysis and satellite-based QuikSCAT dataset (2000–2006), is made over the South Atlantic (10°N–40°S). On a mean seasonal scale, the comparison shows that these three wind stress datasets have qualitatively similar patterns. Quantitatively, in general, from about the equator to 20°S in the mid-Atlantic the wind stress values are stronger in NCEP/NCAR data than those in UWM/COADS data. On the other hand, in the Intertropical Convergence Zone (ITCZ) area the wind stress values in NCEP/NCAR data are slightly weaker than those in UWM/COADS data. In the South Atlantic, between 20° S–40°S, the QuikSCAT dataset presents complex circulation structures which are not present in NCEP/NCAR and UWM/COADS data. The wind stress is used in a numerical ocean model to simulate ocean currents, which are compared to a drifting-buoy observed climatology. The modeled South Equatorial Current agrees better with observations between March–May and June–August. Between December–February, the South Equatorial Current from UWM/COADS and QuikSCAT experiments is stronger and more developed than that from NCEP/NCAR experiment. The Brazil Current, in turn, is better represented in the QuikSCAT experiment. Comparison of the annual migration of ITCZ at 20° and 30°W in UWM/COADS and NCEP/NCAR data sources show that the southernmost position of ITCZ at 30°W in February, March and April coincides with the rainy season in NE Brazil, while the northernmost position of ITCZ at 20°W in August coincides with the maximum rainfall of Northwest Africa.     

Impact of Multisatellite Altimeter Data Assimilation on Wave Analysis and Forecast

Satellite altimetry has become an important discipline in the development of sea-state forecasting or more generally in operational oceanography. Météo-France Marine and Oceanography Division is much involved in altimetry, in which it is also one of the main operational customers. Sea-state forecasts are produced every day with the help of numerical models assimilating Fast Delivery Product altimeter data from ESA ERS-2 satellite, available in real-time (3–5 h). These forecasts are transmitted to seamen as part of safety mission of persons and properties, or specific assistance for particular operations. With the launch of ENVISAT (from ESA, launched on 1 March 2002, to take over the ERS mission) and JASON-1 (from CNES/NASA, launched on 7 December 2001, successor of TOPEX/Poseidon), we have an unprecedented opportunity of improved coverage with the availability in quasi-real-time of data from several altimeters. The objective of this study is to evaluate the impact of using multisources of altimeter data in real-time, to improve wave model analyses and forecasts, at global scale. Since July 2003, Météo-France injects the wind/wave JASON-1 Operational Sensor Data Record on the WMO Global Transmitting System, making them available in near real-time to the international meteorological community. Similarly, fast delivery altimeter data of ENVISAT will improve coverage and contribute to the constant progress of marine meteorology. For this purpose, significant wave height time series were generated using the Wave Model WAM and the assimilation of altimeter wave heights from two satellites ERS-2 and JASON-1. The results were then compared to Geosat Follow-On (GFO, U.S. Navy Satellite) and moored buoy wave data. It is shown that the impact of data assimilation, when two (ERS-2 and JASON-1) or three (ERS-2 with JASON-1 and GFO) sources of data are used instead of one (ERS-2), in term of significant wave height, is larger in wave model analyses but smaller in wave model forecasts. However, there is no improvement in terms of wave periods, both in the analysis and forecast periods.     

Nearshore directional wave measurements by surface-following buoy and acoustic Doppler current profiler

Directional energy spectra of nearshore surface waves were measured for a 3-year period (2004–2007) at a site with mean depth 14 m and mean tidal range 2.1 m. Triaxys surface-following wave buoys reported hourly directional wave energy spectra and wave parameters near the offshore end of the Savannah River Entrance Channel, Georgia, USA. An acoustic Doppler current profiler (ADCP) was located beside the wave buoy for 3 months. Directional and non-directional surface wave energy spectra and the corresponding bulk wave parameters (height, period, and direction) are compared for the two systems. Most parameters derived from the spectra agree closely; the most significant differences were found at the upper and lower frequency measurement limits, where signal-to-noise ratios were lower. The wave buoy consistently reports a small amount of energy below 0.05 Hz that does not appear in the ADCP-derived spectra and does not appear to be related to the mooring system. This leads to larger mean and peak periods reported by the buoy. All directional spectra were computed using the Maximum Entropy Method for both instruments, but the buoy, with spectra derived from six independent time series, provides lower directional resolving power than the ADCP, which utilizes twelve time series. Both systems gave similar results defining mean and peak wave directions, with the primary difference being that the ADCP indicates energy to be more tightly concentrated around the peak direction.     

Testing and verifying the wind wave model with an optimized source function

With the purpose of revealing the actual advantages of the new source function that was earlier proposed in [5] for use in numerical wind wave models, its testing and verification was carried out by means of modification of the WAM (Cycle-4) model. The verification was performed on the basis of a comparison of the results of wave simulation for a given wind field with the buoy observation data obtained in three oceanic regions. In the Barents Sea, this kind of comparison was made for wave observations from a single buoy with an interval of 6 hours for a period of 3 years. In two regions of the North Atlantic, the comparison was performed for 3 buoys in both regions for observation periods of 30 days with an interval of 1 hour. Estimations of the simulation accuracy were obtained for a series of wind wave parameters, and they were compared with the original and modified WAM model. Advantages of the modified model consisting of the enhancement of the calculation speed by 20–25% and a 1.5- to 2-fold increase in the simulation accuracy for the significant wave height and the mean period were proved.     

Validation of the WAMC4 wave model for the Black Sea

The present paper describes the set-up and application of the third-generation wave model — WAM Cycle 4 to the Black Sea. The wind fields are calculated by a regional atmosphere model (REMO), which was driven with the conditions from the global NCEP re-analysis project. These atmospheric data are used to force the state-of-the-art WAM model. The validation is done by comparison of wave model output against directional buoy measurements registered at three deep-water locations and wave gauge data taken at a point in intermediate depth near the Black Sea coast. The results reveal that agreement between modeled and measured data is satisfactory and the quality of the simulations increases under more energetic and severer wind and wave conditions. Following the validation, a 41-year wave hindcast was implemented spanning the period 1958–1998.