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In real-time operational coastal forecasting systems for the northwest European shelf, the representation accuracy of tide-surge models commonly suffers from insufficiently accurate tidal representation, especially in shallow near-shore areas with complex bathymetry and geometry. Therefore, in conventional operational systems, the surge component from numerical model simulations is used, while the harmonically predicted tide, accurately known from harmonic analysis of tide gauge measurements, is added to forecast the full water-level signal at tide gauge locations. Although there are errors associated with this so-called astronomical correction (e.g. because of the assumption of linearity of tide and surge), for current operational models, astronomical correction has nevertheless been shown to increase the representation accuracy of the full water-level signal. The simulated modulation of the surge through non-linear tide-surge interaction is affected by the poor representation of the tide signal in the tide-surge model, which astronomical correction does not improve. Furthermore, astronomical correction can only be applied to locations where the astronomic tide is known through a harmonic analysis of in situ measurements at tide gauge stations. This provides a strong motivation to improve both tide and surge representation of numerical models used in forecasting. In the present paper, we propose a new generation tide-surge model for the northwest European Shelf (DCSMv6). This is the first application on this scale in which the tidal representation is such that astronomical correction no longer improves the accuracy of the total water-level representation and where, consequently, the straightforward direct model forecasting of total water levels is better. The methodology applied to improve both tide and surge representation of the model is discussed, with emphasis on the use of satellite altimeter data and data assimilation techniques for reducing parameter uncertainty. Historic DCSMv6 model simulations are compared against shelf wide observations for a full calendar year. For a selection of stations, these results are compared to those with astronomical correction, which confirms that the tide representation in coastal regions has sufficient accuracy, and that forecasting total water levels directly yields superior results. [ABSTRACT FROM AUTHOR]

Hydrodynamic models are commonly used for predicting water levels and currents in the deep ocean, ocean margins and shelf seas. Their accuracy is typically limited by factors, such as the complexity of the coastal geometry and bathymetry, plus the uncertainty in the flow forcing (deep ocean tide, winds and pressure). In Southeast Asian waters with its strongly hydrodynamic characteristics, the lack of detailed marine observations (bathymetry and tides) for model validation is an additional factor limiting flow representation. This paper deals with the application of ensemble Kalman filter (EnKF)-based data assimilation with the purpose of improving the deterministic model forecast. The efficacy of the EnKF is analysed via a twin experiment conducted with the 2D barotropic Singapore regional model. The results show that the applied data assimilation can improve the forecasts significantly in this complex flow regime. [ABSTRACT FROM AUTHOR]

The Singapore Regional Waters (SRW) is one of the more complex tidal regions in the world. This complexity is caused by various factors including the interaction of the Indian and Pacific oceans with their mainly semi-diurnal and diurnal tide, respectively, complicated coastline geometry, small islands and sharply varying bottom topography. Tidal data analysis is hampered by the lack of reliable coastal stations with long-term water level records while numerical tidal modelling studies suffer from lack of accurate high-resolution bathymetry data and uncertainty in the prescription of the tidal open boundary forcing. The present study combines numerical modelling with available along-track satellite altimetry data and a limited set of reliable coastal stations. It proposes a structured approach to study the sensitivity of tidal propagation and interactions to parameters like the prescription of tidal forcing at the open ocean boundaries, local depth information and seabed roughness. To guide and facilitate this analysis, the open-source software environment OpenDA for sensitivity analysis and simultaneous parameter optimisation is used. In a user-controlled way, the vector difference error in tidal representation could so effectively be reduced by ~50%. The results confirm the benefit of using OpenDA in guiding the systematic exploration of the modelled tide and reducing the parameter uncertainties in different parts of the SRW region. OpenDA is also shown to reduce the repetitive nature of simultaneous parameter variation. Finally, the behaviour of the tide in the region and its sensitivities to changes in tidal boundary forcing and to local depth and friction variation in the narrow regions of the Malacca Strait is now much better understood. With most of the systematic errors reduced in the numerical model as a result of the sensitivity analysis, it is expected that the model can be applied to study tide-surge interaction and is much better suited for later application in combination with data assimilation techniques such as Kalman filtering for which systematic model errors should be minimal. [ABSTRACT FROM AUTHOR]

The first part of the paper describes a portable and flexible data assimilation environment (DATools) for easy application of data assimilation and calibration techniques to models that are used in smaller-scale engineering applications, for example to guide temporary offshore construction works, marine surveying and salvage operations. These applications are characterized by the need for detailed forecasts of often strongly non-linear marine behaviour in shallow-shelf seas under constraints of limited field measurements and absence of large-scale computing facilities. The applications are often prepared, and their results are interpreted by end-user engineers in field offices, not well acquainted with the data assimilation theory. The DATools data assimilation environment has been designed to facilitate such applications. It presently features an ensemble Kalman filter and two particle filters. These can be coupled to any process model in a standardized way through the so-called Published Interface that is used in an increasing number of flood-forecasting applications across Europe. The design of the system and its main modules are discussed, looking at the system from a non-specialist user perspective and focusing on modularity, transparency, user guidance, intuitive and flexible uncertainty prescription. The second part of the paper describes a typical example application of data assimilation in an engineering environment for which the modelling environment has been developed: the daily forecast of current and salinity profiles to guide construction works in Osaka Bay. An ensemble Kalman filter-based steady-state Kalman filter is developed for assimilation of salinity and horizontal currents into an existing three-dimensional flow model for the highly non-linear stratified shallow bay. Calibration results are summarised for both hindcast mode and forecast mode. After assessment of the results, a second, improved model setup is described, including now assimilation of temperature and water level as well as currents and salinity. The calibration results are shown to improve further. Both applications show that significant forecast improvements can be possible with data assimilation in typical engineering applications, notwithstanding operational constraints such as limited measurement data and restricted computational infrastructure. It is argued that the described portable data assimilation environment is well suited for temporary engineering applications as it provides typical end-user functionalities for data assimilation and will so allow the user to focus on the characteristics of the physical processes and interpretation of the results instead of on details of the mathematical techniques. This should make practical data assimilation accessible for a much wider (end) user community in oceanography and offshore. [ABSTRACT FROM AUTHOR]

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications. [ABSTRACT FROM AUTHOR]

Copyright of Huisarts En Wetenschap is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Huisarts En Wetenschap is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)