Predicting tidal heights for new locations using 25 hours in situ sea-level observations plus reference site records: a complete tidal species modulation with tidal constant.
A hybrid technique for predicting tides for new locations, based on as little as 25 h of concurrent temporary and reference site sea level observations, plus up to a year of reference records, is evaluated using 2-yr South Korean and New Zealand case studies. Comparisons are made between the existing prediction methods of conventional standard harmonic analysis and prediction (CSHAP) and tidal species modulation with tidal constant corrections (TSM1TCC). Building on these approaches, a new procedure is developed to produce a complete tidal species modulation (CTSM) equivalent of CSHAP, with the added inclusion of nodal factors and angles, astronomical arguments, and tidal species tidal constant correction terms (1TCC), to generate results for temporary sites. The CTSM1TCC approach described here overcomes the record length limitations of traditional standard harmonic-based prediction methods, making the technique more useful to diverse coastal and hydrographic researchers. The CTSM1TCC method is refined using yearlong input and comparative data from contrasting hydrographic settings, revealing spring periods, specific months, and conditions devoid of nontidal residual extremes (e.g., storms) as the most appropriate sample periods for collecting temporary site data in order to maximize prediction accuracy. CTSM1TCC represents a viable alternative to tidal prediction methods using multiconstituent inferences, for those wishing to make predictions for new sites based on established conventional tidal prediction software, with the added benefits of efficient input data collection and no need for a decision process regarding multiconstituent inference calculations. CTSM1TCC could, without compromising accuracy, support the spatial and temporal proliferation of tidal predictions across coastal oceans, where fieldwork funds and instruments currently hinder predictions for new locations.