Tidal Prediction Research Articles

Tidal prediction using time series analysis of Buoy observations

Tidal prediction using time series analysis of Buoy observations

A Modified Nonstationary Tidal Harmonic Analysis Model for the Yangtze Estuarine Tides

AbstractInfluenced by river discharge, the tidal properties of estuarine tides can be more complex than those of oceanic tides, which makes the tidal prediction less accurate when using a classical tidal harmonic analysis approach, such as the T_TIDE model. Although the nonstationary tidal harmonic analysis model NS_TIDE can improve the accuracy for the analysis of tides in a river-dominated estuary, it becomes less satisfactory when applying the NS_TIDE model to a mesotidal estuary like the Yangtze estuary. Through the error source analysis, it is found that the main errors originate from the low frequency of tidal fluctuation. The NS_TIDE model is then modified by replacing the stage model with the frequency-expanded tidal–fluvial model so that more subtidal constituents, especially the “atmospheric tides,” can be taken into account. The results show that the residuals from tidal harmonic analysis are significantly reduced by using the modified NS_TIDE model, with the yearly root-mean-square-error values being only 0.04–0.06 m for the Yangtze estuarine tides.

High-Precision Combined Tidal Forecasting Model

To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient.

A preliminary geophysical analysis of gravity time-variation in Daejeon

A gravity time series slightly longer than a month acquired by using a Burris gravimeter at Daejeon, Korea has been analyzed. After removal of linear drift of the gravimeter, the observed data was found to closely match with the body tide prediction except; (i) small phase differences of each main tidal constituents and (ii) existence of long period component in the observed data. The r.m.s. observed gravity tide variation was 62.4 μGal. The overall g-factor was found as 1.165. The values of g-factor for the four main tidal constituents; M{nt2}, S2, O1, K1, were found as 1.166, 1.172, 1.168, and 1.142 respectively. The r.m.s. value of ocean tidal loading effect on gravity at Daejeon was 0.95 μGal during the time span. Both the rainfall and the atmospheric pressure change have induced gravity variations of amplitude of about 10 to 1 μGal. The long period component, which is clearly found in the observed spectrum, while not existent in the prediction, is possibly caused by the local ground water variation. It is desirable to have a few number of permanent gravity stations operating in Korean peninsula.

A method for detecting and locating the gross error of tidal based on wavelet analysis

Tidal harmonic constants are necessary data for the evaluation of tidal model, tidal prediction and chart datum. Compared with the wavelet multi-scale analysis model, a new method is put forward to detect and locate tidal discrete and continuous gross error. Based on the properties of the wavelets, the wavelet suitable for detecting the gross error of tidal is selected. And taking db6 wavelet as an example, the feasibility and effectiveness of this method are proved by experiments. The results show that the method can not only simultaneously detect and locate discrete gross error and continuous gross based on high frequency information, and can detect and locate the systematic deviation caused by the zero drift according to the low frequency information. Experimental result shows that the method is more simple, the efficiency and accuracy of detecting and locating gross error are improved.

The simulation of the observation data in predicting tidal patterns using the Admiralty method in Dumai’s harbour

In this paper, we have discussed the tidal analysis conducted using the Admiralty method with 15 days of tidal data as the research object. Due to the long tidal data period, the analysis was conducted in order to determine the proper time to collect the data for future tidal predictions. This data has to be adequate for its realisation in reality. The used data was collected from the Geospatial Information Association and the location of this research study was Dumai’s Harbour. The study started on July 13th, 2014 and ended on December 8th, 2014. For verification, the used data started from June 13th, 2016 and ended October 31st, 2016. From the 135 simulations, we determined that the magnitude of the Root Mean Square Error (RMSE) was 106.2. In order to produce well-predicted tidal results, the collection of the tidal data was best started during the phase of the crescent moon (which begins on the 24th based on the Hijriah calendar). On these dates, the RMSE reached the lowest value with a magnitude of 74.76. This result could be used as a reference when choosing the proper time to collect the observation data and also for predicting the tidal patterns at the location.

A NOVEL HIGH-RESOLUTION STORM SURGE FORECAST FOR THE GERMAN BIGHT

Storm surges are one of the most dangerous natural hazards in coastal areas and have the ability to cause great damages including fatalities. To be prepared when another storm surge hits the coast, reliable storm surge forecasts are indispensable. Storm surge warnings are routinely provided for selected tide gauge locations along a coastline through state-of-the-art forecast systems. In Germany, the Federal Maritime and Hydrographic Agency (BSH) (in cooperation with the German Weather Service (DWD)) have the responsibility for storm surge forecasts and warnings along the German North and Baltic Sea coastlines. The operational system in place for the North Sea consists of numerical weather forecast systems, a surge model and model output statistics. It provides accurate high frequency water level forecasts up to six days ahead at selected tide gauge sites (Müller-Navarra and Knüpfer, 2010), but not for the coastline in between. Spatial forecasts are, however, currently not available for two reasons: first, the shallow coast with complex morphological structures leads to strong non-linearities between individual sites hampering simple interpolation schemes (Arns et al. 2015). Second, tidal predictions are limited to tide gauge locations, which do not fall dry during low tide, since the traditional estimation of tidal coefficients requires complete time series covering both low and high waters.

EFFECTS OF HYPERCONCENTRATION-RELATED DRAG REDUCTION ON TIDAL PROPAGATION IN THE QIANTANG ESTUARY

The Qiantang Estuary (QE) in the east coast of China is famous for its hyperconcentrated tidal bore with the highest sediment concentration up to about 80 kg/m3 (Pan et al., 2013). Recent research for some European estuaries has indicated that hyperconcentrated environment may affect tidal propagation through hydraulic drag reduction, resulting in an enlarged tidal range and even a regime shift in the estuary (Winterwerp and Wang, 2013). However, this water-sediment interactions have not been considered in the tidal prediction of the Qiantang Estuary in the past. Instead, a bed roughness even smaller than that of glossy glass has often been used to fit the observed tidal data in the mathematical modelling.
 
 Therefore, the present study aims to reveal whether the hyperconcentration-related drag reduction can explain the mechanism of very small bed roughness in the Qiantang Estuary and to what extent it can affect the tidal propagation especially for the tidal range. First, an analytical model is used to predict the tidal range along the QE (from Wenyan to Ganpu stations) for different methods of bed friction. Then, mathematical modelling is conducted to shed light on the processes of tidal wave propagation for the corresponding approaches. The results of the two models are compared together with the field observations to further verify the ability of the analytical model and the significance of the effects of hyperconcentration-related drag reduction.

Towards robust pan-European storm surge forecasting

Operational forecasting systems are important for disaster risk reduction. In this work we implement a coupled storm surge and tidal model on an unstructured grid over Europe towards the development of a pan-European Storm Surge Forecasting System (EU-SSF). The skill to predict tidal, surge and total water levels was evaluated based on measurements from 208 tidal gauge stations. Results show satisfactory performance for the two atmospheric forcing datasets tested, a High Resolution Forecast and ERA-INTERIM reanalysis, both provided by the European Center for Medium range Weather Forecast. For tidal predictions, the total RSS is equal to 0.197 m, lower than the values estimated by the global tidal model FES2004, and outperformed only by FES2012 (RSS = 0.05 m), which however is a product of data assimilation. Storm surge validation results show good predictive skill, with 0.04 m < RMSE < 0.21 m and %RMSE within 4%–22%. Coupling with tides results in improved storm surge level predictions, with RMSE reducing by up to 0.033 m. The areas benefiting most from the coupling are the North Sea and the English Channel, resulting in up to 2% reduction of the %RMSE. Increasing the resolution of atmospheric forcing also improves the predictive skill, leading to a reduction of RMSE up to 0.06 m in terms of the extremes, especially in shallow areas where wind is the main driver for surge production. We propose a setup for operational pan-European storm surge forecasting combining tidal levels from the FES2012 model and storm surge residuals from the EU-SSF setup which couples meteorological and astronomic tides.

Radiational tides: their double-counting in storm surge forecasts and contribution to the Highest Astronomical Tide

Abstract. Tide predictions based on tide-gauge observations are not just the astronomical tides; they also contain radiational tides – periodic sea-level changes due to atmospheric conditions and solar forcing. This poses a problem of double-counting for operational forecasts of total water level during storm surges. In some surge forecasting, a regional model is run in two modes: tide only, with astronomic forcing alone; and tide and surge, forced additionally by surface winds and pressure. The surge residual is defined to be the difference between these configurations and is added to the local harmonic predictions from gauges. Here we use the Global Tide and Surge Model (GTSM) based on Delft-FM to investigate this in the UK and elsewhere, quantifying the weather-related tides that may be double-counted in operational forecasts. We show that the global S2 atmospheric tide is captured by the tide-and-surge model and observe changes in other major constituents, including M2. The Lowest and Highest Astronomical Tide levels, used in navigation datums and design heights, are derived from tide predictions based on observations. We use our findings on radiational tides to quantify the extent to which these levels may contain weather-related components.

Effects of Radial Sand Ridges on tidal process in the South Yellow Sea

The South Yellow Sea continental shelf, with a wide area of shallow waters, is located at the intersection of the rotating tidal wave system in the west of the South Yellow Sea and the tidal wave system in the East China Sea. The South Yellow Sea has a unique radiation sandbar terrain, and there are large currents of sand ridges and current channels. Most of the current researches focus on the development and evolution mechanisms of Radial Sand Ridges. However, there are few studies on the influence of tidal waves on terrain in this region. In this paper, the three-dimensional numerical model FVCOM (Finite Volume Coastal Ocean Model) was used to simulate the tidal propagation in the South Yellow Sea and was verified with measurements. This paper compares the changes in the tidal wave of the South Yellow Sea with and without the effect of Radial Sand Ridges to analyze the topographic effect. The changes of tidal wave shape, tidal current velocity, the redistribution of tidal energy and the distribution of tidal mixed zone were analyzed. This research has important implications for the prediction of tides, the development of fisheries and the maintenance of the ecological environment of the South Yellow Sea. The results show that the shape of tidal wave is distorted by the radial sand ridges: mainly, the duration of tidal ebbing is longer than that of tidal flooding. The distortion becomes distinct as the tidal wave approaches the shore, implying that the closer the tidal wave gets to the shore, the greater the nonlinear effect will be. The presence of the Radial Sand Ridges makes the flow rate of the radial inlet larger than 1 m/s, and the flow rate of both the rising tide and the ebbing tide is greater than that of the Waste Yellow River Estuary and the South Yangtze Estuary. At the same time, the existence of the special terrain enlarges the tidal energy flux, speeds up the tidal energy dissipation, and also makes the critical values of the tidal mixing front closer to the shore, namely the tidal mixing zone becoming shoreward. The Radial Sand Ridges also turns down the value of nearshore-stratification parameter. The sea-water blending is more severe in this area, compared to an idealized case with a flat shelf covering the South Yellow Sea. The range of influence of Radial Sand Ridges on tidal current velocity, tidal energy flux, tidal energy dissipation and tidal front position is relatively uniform. It has affected from the eastern part of the Shandong peninsula (around 122°E) across the Radial Sand Ridges to the southern Yangtze River estuary.

An Ensemble Real-Time Tidal Level Prediction Mechanism Using Multiresolution Wavelet Decomposition Method

Precise real-time tidal prediction is essential for management of marine activities. Note that the tidal change is a complex time-varying nonlinear process, which is not only generated by periodic configurations of celestial bodies but also influenced by various time-varying meteorological factors. To achieve precise real-time tidal prediction, an ensemble tidal prediction mechanism is established by combining harmonic analysis and variable neural networks which are constructed by discrete wavelet transform (DWT). In the ensemble prediction mechanism, a conventional harmonic analysis method is used for representing the effects of celestial factors, while a DWT-based variable neural network is used for representing the nonlinear time-varying influences of meteorological factors and other unmodeled factors. The decomposition of tidal residual time series enables the precise prediction of time-varying dynamics by using a variable neural network whose dimensions and parameters are both adaptively tuned online. High accuracy of the proposed ensemble real-time tidal prediction mechanism is demonstrated by simulation studies on the actual tidal measurements collected from the Old Port Tampa tidal station and other four tidal stations in the USA.

Baroclinic Tidal Sea Level from Exact-Repeat Mission Altimetry

AbstractA near-global model for the sea surface expression of the baroclinic tide has been developed using exact-repeat mission altimetry. The methodology used differs in detail from other altimetry-based estimates of the open ocean baroclinic tide, but it leads to estimates that are broadly similar to previous results. It may be used for prediction of the baroclinic sea level anomaly at the frequencies of the main diurnal and semidiurnal tides , , , and , as well as the annual modulates of , denoted and . The tidal predictions are validated by computing variance reduction statistics using independent sea surface height data from the CryoSat-2 altimeter mission. Typical midocean baroclinic tidal signals range from a few millimeters to centimeters of elevation, corresponding to subsurface isopycnal displacements of tens of meters; however, in a few regions, larger signals are present, and it is found that the present model can explain more than 13-cm2 variance at some sites. The predicted tides are also validated by comparison with a database of hourly currents inferred from drogued surface drifters. The database is large enough to permit assessment of a simple model for scattering of the low-mode tide. Results indicate a scattering time scale of approximately 1 day, consistent with a priori estimates of time-variable refraction by the mesoscale circulation.

Prediction of tidal currents using Bayesian machine learning

We propose the use of machine learning techniques in the Bayesian framework for the prediction of tidal currents. Computer algorithms based on the classical harmonic analysis approach have been used for several decades in tidal predictions, however the method has several limitations in terms of handling of noise, expressing uncertainty, capturing non-sinusoidal, non-harmonic variations. There is a need for principled approaches which can handle uncertainty and accommodate noise in the data. In this work, we use Gaussian processes, a Bayesian non-parametric machine learning technique, to predict tidal currents. The probabilistic and non-parametric nature of the approach enables it to represent uncertainties in modelling and deal with complexities of the problem. The method makes use of kernel functions to capture structures in the data. The overall objective is to take advantage of the recent progress in machine learning to construct a robust algorithm. Using several sets of field data, we show that the machine learning approach can achieve better results than the traditional approaches.

A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network

ABSTRACTReal-time prediction of tidal level is of great significance for activities of human beings in the fields of marine and coastal engineering. However, the disturbance factors of tidal level are very intricate, which deteriorate the tidal prediction accuracy. To improve the accuracy of real-time tidal-level prediction, a modular real-time tidal-level prediction approach is proposed based on the grey group method of data handling (Grey-GMDH) neural network. The modular model is composed of astronomical tide parts caused by celestial bodies’ movement and the nonastronomical tide parts caused by various meteorological and other environmental factors. The GMDH is a polynomial network that is commonly used in prediction and pattern recognition. However, GMDH is sensitive to nondeterministic time series, which would result in low accuracy of prediction. In this study, the grey prediction theory is introduced into the GMDH prediction model to alleviate the unfavorable effects of uncertainty caused by various environmental factors and the adverse effects caused thereby on the prediction accuracy. In this study of tidal prediction, the Grey-GMDH model is used to predict the nonastronomical tide parts, whereas the conventional harmonic analysis model is used to predict the astronomical tide parts. The final prediction result is achieved by combining the estimation outputs of the harmonious analysis model and the Grey-GMDH model. Measured tidal-level data of San Diego tidal station is selected as the testing database. Simulation and experimental results confirm that the proposed approach can achieve real-time predictions for tidal level with high accuracy, satisfactory convergence and stability.

GIS-based seamless tide prediction framework in the Persian Gulf

ABSTRACTPrediction of international water tides induced by gravitational centripetal forces of the Earth–Sun–Moon system is vital in various applications. Available methodologies and software that offer observation analysis and tide level prediction often support limited points of interest (mainly islands and ports), and they are mostly based on Harmonic Analysis algorithms. The present study proposes a geographical information system-based framework in order to store and retrieve tide information derived from co-tidal charts such as amplitude and phase charts. By retrieving the exact corresponding information of any point in Persian Gulf, the developed programme makes continuous tide prediction possible for every area of interest, including offshore locations, island boundaries, and alongside coasts of Persian Gulf’s states.

Hybrid harmonic analysis and wavelet network model for sea water level prediction

Accurate sea water level prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction is commonly carried out using only the harmonic analysis (HA-only) model or only a wavelet network (WN-only) model. The harmonic analysis method is the most reliable model for long term sea water level prediction when long data records are available and in contrast the wavelet network method is the most reliable model used for short term sea water level prediction when short data records are available. This paper developed a hybrid harmonic analysis and wavelet network (HA-and-WN) model for accurate sea water level prediction. To validate the hybrid HA-and-WN model, sea water level data from four tide gauges are employed to investigate the performance of the developed hybrid model. It is shown that the majority of error values at 95% confidence level fall within ±14.77cm, ±2.65cm and ±2.08cm range in average with maximum error of 36.84cm, 9.21cm and 7.00cm in average for HA-only model, WN-only model and hybrid HA-and-WN model, respectively. Also, it is found that the root-mean-squared (RMS) errors are about 9.75cm, 1.85cm and 1.49cm for HA-only, WN-only and hybrid HA-and-WN models, respectively, based on the overall performance from the four tide gauges under implementation. Therefore, it is concluded that the developed hybrid HA-and-WN model is superior to the HA-only model by about 85% and outperforms the WN-only model by about 20%, based on the overall RMS errors.

Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms

The prediction of the dinoflagellate red tide forming Karenia selliformis is a relevant task to aid optimized management decisions in marine coastal water. The objective of the present study is to compare different modeling approaches for prediction of Karenia selliformis occurrences and blooms. A set of physical parameters (salinity, temperature and tide amplitude), meteorological constraints (evaporation, air temperature, insolation, rainfall, atmospheric pressure and humidity), sampling months and sampling sites are used. The model prediction included general linear model (GLM), Bayesian Network (BN) and the simplest BN type which is, Naive Bayes classifier (NB). The results showed that three models incriminated high salinity in Karenia selliformis blooms and the sampling sites, mainly Boughrara lagoon, in the occurrences. The BN performed better than linear models (NB and GLM) for both Karenia selliformis occurrences and blooms prediction. This later is related to the facts that BN considered the inter-independency between predictive variables and that the relationships between the variables and the outcome are often non-linear such us; the transition to bloom situations appeared to be triggered by a salinity threshold. This study is useful in the management of this ecosystem so as to use the best disposal options in the early prediction of the toxic blooms.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system (ANFIS) model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules: the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system (FIS) structure, three approaches which include grid partition (GP), fuzzy c-means (FCM) and sub-clustering (SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

ACCURACY ASSESSMENT OF RECENT GLOBAL OCEAN TIDE MODELS AROUND ANTARCTICA

Abstract. Due to the coverage limitation of T/P-series altimeters, the lack of bathymetric data under large ice shelves, and the inaccurate definitions of coastlines and grounding lines, the accuracy of ocean tide models around Antarctica is poorer than those in deep oceans. Using tidal measurements from tide gauges, gravimetric data and GPS records, the accuracy of seven state-of-the-art global ocean tide models (DTU10, EOT11a, GOT4.8, FES2012, FES2014, HAMTIDE12, TPXO8) is assessed, as well as the most widely-used conventional model FES2004. Four regions (Antarctic Peninsula region, Amery ice shelf region, Filchner-Ronne ice shelf region and Ross ice shelf region) are separately reported. The standard deviations of eight main constituents between the selected models are large in polar regions, especially under the big ice shelves, suggesting that the uncertainty in these regions remain large. Comparisons with in situ tidal measurements show that the most accurate model is TPXO8, and all models show worst performance in Weddell sea and Filchner-Ronne ice shelf regions. The accuracy of tidal predictions around Antarctica is gradually improving.