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A solution for the correct interpolation of data across regions with abrupt changes in tidal phase-lag 总被引：1，自引：0，他引：1
This paper provides an explanation of an automated solution for correctly interpolating phase-lags across abrupt boundaries. Although an automated solution to this problem has existed for several years, this is not commonly known and so many researchers continue to perform corrections manually. Interpolation errors commonly occur when tidal propagation surfaces are generated for regimes with amphidromic points. General correction methods are manual, clunky and prone to operator error. The problem can be solved by applying a simple method to scalarize the phase-lag vectors pre-interpolation. This approach successfully and automatically generates correct tidal phase-lag interpolation values and may be applied to any surface mapping software used to interpolate phase-lags. 相似文献
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Ocean Science Journal - A minimum 19 year tidal prediction dataset covering nodal (satellite) modulation effects is required to determine the Lowest Astronomical Tide (LAT) and Highest Astronomical... 相似文献
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This study provides a practical guide to the use of classical tidal prediction algorithms in coastal numerical forecasting models such as tide and tide-storm-surge models. Understanding tidal prediction parameter formulas and their limitations is key to successfully modifying and upgrading tidal prediction modules in order to increase the accuracy of perpetual interannual simulations and, in particular, storm-surge modeling studies for tide-dominated coastal environments. The algorithms for the fundamental prediction parameters, the five astronomical variables, used in tidal prediction are collated and tested. Comparisons between their estimation using different parameterizations shows that these methods yield essentially the same results for the period 1900–2099, revealing all are applicable for tidal forecasting simulation. Through experiments using a numerical model and a harmonic prediction program, the effects of nodal modulation correction and its update period on prediction accuracy and sensitivity are examined and discussed using a case study of the tidally-dominated coastal regime off the west coast of Korea. Results indicate that this correction needs updating within <30 days for accurate perpetual interannual tidal and mean sea-level predictions, and storm-surge model predictions requiring centimeter accuracy, for tidally-dominated coastal regimes. Otherwise, unacceptable systematic errors occur. 相似文献
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The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS. 相似文献
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