scholarly journals Research on tidal harmonic analysis based on correlation observation error

2020 ◽  
Vol 1453 ◽  
pp. 012138
Author(s):  
Jin Zhang ◽  
ShuJun Li ◽  
HaiGang Jiang
Keyword(s):  
Harmonic Analysis ◽  
Observation Error ◽  
Tidal Harmonic

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

2019 ◽  
Vol 36 (4) ◽  
pp. 513-525 ◽  
Author(s):  
Min Gan ◽  
Yongping Chen ◽  
Shunqi Pan ◽  
Jiangxia Li ◽  
Zijun Zhou
Keyword(s):  
Harmonic Analysis ◽  
Low Frequency ◽  
Yangtze Estuary ◽  
Atmospheric Tides ◽  
Source Analysis ◽  
Analysis Model ◽  
Tidal Fluctuation ◽  
The Yangtze Estuary ◽  
Tidal Harmonic ◽  
Oceanic 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.

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

2021 ◽  
Vol 14 (3) ◽  
pp. 1445-1467
Author(s):  
Gregory C. Smith ◽  
Yimin Liu ◽  
Mounir Benkiran ◽  
Kamel Chikhar ◽  
Dorina Surcel Colan ◽  
...  
Keyword(s):  
Harmonic Analysis ◽  
Gulf Stream ◽  
Water Mass ◽  
Grid Resolution ◽  
Prediction System ◽  
Sea Level Anomaly ◽  
Tidal Harmonic ◽  
Mass Properties ◽  
Ocean Prediction ◽  
Analysis System

Abstract. 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.

scholarly journals HIGH AND LOW WATER PREDICTION AT LAGOS HARBOUR, NIGERIA

2017 ◽  
Vol 36 (3) ◽  
pp. 944-952
Author(s):  
OT Badejo ◽  
SO Akintoye
Keyword(s):  
Prediction Model ◽  
White Noise ◽  
Harmonic Analysis ◽  
Least Squares ◽  
Water Level ◽  
Observational Period ◽  
Tidal Constituent ◽  
Tidal Harmonic ◽  
Tidal Data ◽  
Tidal Constituents

In this work, 500 hourly water level tidal data were used to perform least squares tidal harmonic analysis. Eleven tidal constituents were used for the harmonic analysis. Astronomical arguments (v + u) and the nodal factor (f) were computed for each tidal constituent and at each observational period with a programme written in Matlab environment. The harmonic constants determined from the least squares tidal harmonic analysis were substituted into a tidal prediction model to predict hourly tidal data and tidal predictions at 5 minutes’ intervals. Series of high and low water heights from the tidal predictions made at 5 minutes’ intervals were determined and matched with their corresponding times. Autocorrelation at lags 1 to 30 for the residuals of the observed and predicted tidal data shows that there is no significant correlation in the range of the 30 lags. The series of residuals of the observed and predicted tidal data is therefore white noise.   http://dx.doi.org/10.4314/njt.v36i3.39

Unmixing: A new direction from classical tidal harmonic analysis?

2015 ◽  
Author(s):  
Yu Li ◽  
Shengdong Xie ◽  
Bikong Wang ◽  
Shijun He ◽  
Dongmei Huang ◽  
...  
Keyword(s):  
Harmonic Analysis ◽  
Tidal Harmonic

Enhancing tidal harmonic analysis: Robust (hybrid ) solutions

2009 ◽  
Vol 29 (1) ◽  
pp. 78-88 ◽  
Author(s):  
Keith E. Leffler ◽  
David A. Jay
Keyword(s):  
Harmonic Analysis ◽  
Tidal Harmonic ◽  
Hybrid Solutions

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

2021 ◽  
Vol 12 (3) ◽  
pp. 130
Author(s):  
Annas Wahyu Ramadhan ◽  
Didit Adytia ◽  
Deni Saepudin ◽  
Semeidi Husrin ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Deep Learning ◽  
Harmonic Analysis ◽  
Sea Level ◽  
Short Term Memory ◽  
Development Planning ◽  
Coastal Development ◽  
Sea Level Anomalies ◽  
Level Data ◽  
Tidal Harmonic ◽  
Hourly Data

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.

Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE

2002 ◽  
Vol 28 (8) ◽  
pp. 929-937 ◽  
Author(s):  
Rich Pawlowicz ◽  
Bob Beardsley ◽  
Steve Lentz
Keyword(s):  
Harmonic Analysis ◽  
Error Estimates ◽  
Tidal Harmonic
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