Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cantconsequences in coastal operations, such as naval engineering and navigation. Conventional sealevel predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elementsand require long-term historical sea level data. In this paper, two deep learning approachesare applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), andthe second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (InexpensiveDevice for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained themodel for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1stMay to 1st August. We compared forecasting results with RNN and LSTM with the results of theconventional method, namely tidal harmonic analysis. The LSTM’s results showed better performancethan the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 andan RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodatenon-tidal harmonic data such as sea level anomalies.

The Impact of the Mesoscale Ocean Variability on the Estimation of Tidal Harmonic Constants Based on Satellite Altimeter Data in the South China Sea

The estimation accuracy of tidal harmonic constants is of great significance to maritime traffic and port construction. However, due to the long sampling period of satellite altimeters, tidal signals alias the mesoscale ocean frequencies. As a result, the harmonic analysis is affected by mesoscale environmental noise. In this study, the influence of the mesoscale ocean variability (MOV) on the estimation of tidal harmonic constants was quantified by analyzing 25 years of altimeter data from the Topex/Poseidon (T/P) and Jason satellites in the South China Sea (SCS). The results indicated that the absolute amplitude differences (AADs) of the eight major tidal constituents before and after the mesoscale variability correction (MVC) were generally within 10 mm, and most were within 6 mm. For the relative impact, M2, O1, and K1 were not obviously affected by the MOV because of their large amplitudes, and the AADs generally accounted for less than ±10% of the amplitudes. As a tidal constituent with amplitude less than 2 cm in the SCS, the amplitude of K2 was significantly affected by the MOV, with the ratios of the AADs to its own amplitudes ranging from −64.79% to 95.99% in space. In terms of phase, the K2 tide was most affected by the MOV: 63% of the data points before and after correction were over ±5°, and the maximum and minimum values were 86.46° and −176.27°, respectively. The absolute phase differences of other tidal constituents before and after the MVC were generally concentrated within ±5°. The impact of the MOV on the evolution of tidal amplitudes in the SCS was also explored. It was found that the MOV can cause pseudo-rapid temporal variations of tidal amplitudes in some regions of the SCS.

The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system using an online tidal harmonic analysis

Canada has the longest coastline in the world and includes diverse ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the east coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas in support of marine activities including emergency response, search and rescue, and safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26∘ N in the Atlantic Ocean through the Arctic Ocean to 44∘ N in the Pacific Ocean, with a model grid resolution that varies between 3 and 8 km. RIOPSv2 includes a multivariate data assimilation system based on a reduced-order extended Kalman filter together with a 3D-Var bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration, allowing inhomogeneous anisotropic multivariate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and allow for the time-varying harmonic constants necessary in seasonally ice-infested waters. Compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature (SST). In addition to the tidal harmonic analysis, the observation operator for sea level anomaly (SLA) is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found near the Gulf Stream for all model fields due to the higher model grid resolution, with smaller root mean squared (rms) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 ∘C for SST. Verification against along-track satellite observations demonstrates the improved representation of mesoscale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced rms differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–2019) provide a rare glimpse at errors in Arctic water mass properties and show average salinity biases over the upper 500 m of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.

Hydro-Oceanographic and Bathymetric Survey in Tanjung Merah as a Basis for Modelling Coastal Spatial Plans of Bitung City

The purpose of this study was to examine the parameters of Hydro-Oceanography (Tidal and Tidal Currents) and Bathymetry (Sea Depth) in Tanjung Merah, Bitung City. The method used in this research is a field survey method. The survey was carried out at the research location, precisely in Tanjung Merah, Matuari District, Bitung City. Equipment such as GPS Fish Finder, Floater Current Meter, Tidal Pole and Boats are supporting devices in obtaining accurate data on tides, tidal currents, and bathymetry in the waters of Tanjung Merah. The results showed that the tidal current conditions at the study site with several variations in the depth of the tidal current velocity ranged from 0.2 – 0.3 cm/sec with the dominant direction of the current towards the south. While the tides using tidal harmonic analysis for 15 days of observation, the Formzal Index is 0.93 which means that the tides in this location are mixed type with a single daily trend. Bathymetric conditions in Tanjung Merah identified the topography of the bottom of the waters is steep with depth variations between 2 -30 meters along the coast of Tanjung Merah while shallow areas are found at the mouth of the Tanjung Merah river.

The Regional Ice Ocean Prediction System v2: a pan-Canadian ocean analysis system

Canada has the longest coastline in the world and includes a diversity of ocean environments, from the frozen waters of the Canadian Arctic Archipelago to the confluence region of Labrador and Gulf Stream waters on the East Coast. There is a strong need for a pan-Canadian operational regional ocean prediction capacity covering all Canadian coastal areas, in support of marine activities including emergency response, search and rescue as well as safe navigation in ice-infested waters. Here we present the first pan-Canadian operational regional ocean analysis system developed as part of the Regional Ice Ocean Prediction System version 2 (RIOPSv2) running in operations at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The RIOPSv2 domain extends from 26° N in the Atlantic Ocean through the Arctic Ocean to 44° N in the Pacific Ocean, with a model grid-resolution that varies between 3 and 8 km. RIOPSv2 includes a multi-variate data assimilation system based on a reduced-order extended Kalman filter together with a 3DVar bias correction system for water mass properties. The analysis system assimilates satellite observations of sea level anomaly and sea surface temperature, as well as in situ temperature and salinity measurements. Background model error is specified in terms of seasonally varying model anomalies from a 10-year forced model integration allowing inhomogeneous anisotropic multi-variate error covariances. A novel online tidal harmonic analysis method is introduced that uses a sliding-window approach to reduce numerical costs and to allow time-varying harmonic constants, necessary in seasonally ice-infested waters. As compared to the Global Ice Ocean Prediction System (GIOPS) running at CCMEP, RIOPSv2 also includes a spatial filtering of model fields as part of the observation operator for sea surface temperature. In addition to the tidal harmonic analysis, the observation operator for sea level anomaly is also modified to remove the inverse barometer effect due to the application of atmospheric pressure forcing fields. RIOPSv2 is compared to GIOPS and shown to provide similar innovation statistics over a 3-year evaluation period. Specific improvements are found in the vicinity of the Gulf Stream for all model fields due to the higher model grid-resolution, with smaller root-mean-squared (RMS) innovations for RIOPSv2 of about 5 cm for SLA and 0.5 °C for SST. Verification against along-track satellite observations demonstrates the improved representation of meso-scale features in RIOPSv2 compared to GIOPS, with increased correlations of SLA (0.83 compared to 0.73) and reduced RMS differences (12 cm compared to 14 cm). While the RIOPSv2 grid resolution is 3 times higher than GIOPS, the power spectral density of surface kinetic energy provides an indication that the effective resolution of RIOPSv2 is roughly double that of the global system (35 km as compared to 66 km). Observations made as part of the Year of Polar Prediction (2017–19) provide a rare glimpse at errors in Arctic water mass properties and show salinity biases of 0.3–0.4 psu in the eastern Beaufort Sea in RIOPSv2.

Variational Data Assimilation of Tides

This paper presents an incremental variational method to assimilate the observed tidal harmonic constants using a frequency domain linearized shallow water equation. A cost function was constructed with tidal boundary conditions and tidal forcing as its control (independent) variables. To minimize the cost function, optimal boundary conditions and tidal forcing were derived using a conventional dual 4-Dimensional Variational (4D-Var) Physical-space Statistical Analysis System. The tangent linear and adjoint model were solved by using a finite element method. By adapting the incremental form, the variational method streamlines the workflow to provide the incremental correction to the boundary conditions and tidal forcing of a hydrodynamic forward model. The method was tested for semi-diurnal M2 tides in a regional sea with a complex tidal system. The results demonstrate a 65–72% reduction of tidal harmonic constant vector error by assimilating the observed M2 tidal harmonic constants. In addition to improving the tides of a hydrodynamic model by optimizing boundary conditions and tidal forcing, the method computes a spatially varying uncertainty of individual tidal constituents in the model. The method provides a versatile tool for mapping the spatially continuous tides and currents in coastal and estuarine waters by assimilating the harmonic constants of individual tidal constituents of observed tides and currents.

ANALISIS PASANG SURUT MENGGUNAKAN METODE LEAST SQUARE DI WILAYAH PERAIRAN ENDE, NUSA TENGGARA TIMUR

Abstrak Telah dilakukan penelitian tentang analisis pasang surut menggunakan metode Least Square di wilayah perairan Ende, Nusa Tenggara Timur. Penelitian ini bertujuan untuk menentukan komponen harmonik pasang surut menggunakan metode Least Square serta menganalisis tipe pasang surut menggunakan bilangan Formzahl. Berdasarkan hasil analisis data, diketahui bahwa komponen pasang surut yaitu M2, S2, N2, K1, O1, Q1 dan tipe pasang surut di perairan Ende termasuk tipe campuran condong keharian ganda (mixed tide prevailing semidiurnal) dengan nilai Formzhal (F) setiap bulannya adalah (0,38), (0,31), (0,25), (0,28), (0,38), (0,44), (0,41), (0,33), (0,25), (0,27) (0,35) dan 0,42. Hasil analisis pasang surut menunjukkan nilai amplitudo harmonik pasang surut ganda utama M2 dan S2 lebih dominan dibandingkan komponen pasang surut tunggal utama K1 dan O1. Kata Kunci: Least Square, Campuran Condong Harian Ganda, Perairan Ende. Abstract Research on tidal analysis has been done using the Least Square method in the sea area surrounding Ende, East Nusa Tenggara. This research aims to determine the components of tidal harmonics using the Least Square method and to analyze the type of tides using Formzhal number. Based on the results of analysis data, it is known that tidal harmonic components are M2, S2, N2, K1, O1, Q1 and the type of tides in Ende sea included mixed type of tide prevailing semidiurnal with Formzhal (F) value of (0.38), (0.31), (0.25), (0.28), (0.38), (0.44), (0.41), (0.33), (0.25), (0.27), (0.35), and 0.42. The results show the amplitude of the main tidal component M2 and S2 is the dominant component in the Ende Sea compared to the main single tidal component K1 and O1. Keywords: Least Square, Mixed Tide Prevailing Semidiurnal, Ende Sea.

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

Influenced 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.