Balancing Accuracy and Efficiency in the Temporal Resampling of Met-Ocean Data

Harmonising heterogeneous met-ocean time series to a common temporal resolution is a prerequisite for integrated marine renewable energy assessments. Such datasets often differ in their sampling frequency, statistical distribution, and non-stationarity, complicating joint analysis. This study presents a practical multi-criteria framework for selecting temporal interpolation strategies for met-ocean datasets, explicitly balancing prediction accuracy and computational efficiency. Six environmental variables relevant to offshore renewable energy—wind speed, significant wave height, energy period, peak period, global horizontal irradiance, and upper-ocean thermal gradients—are analysed using ten-year reanalysis datasets for the Madeira Archipelago. Six commonly used deterministic time-domain interpolation methods are evaluated within a unified validation framework combining training–test splits, k-fold cross-validation, and Monte Carlo resampling. Their performances are quantified using the relative root mean square error and computational time, integrated through a composite performance score. The results show that makima interpolation provides the most consistent compromise between accuracy and efficiency for most variables in dense, regularly sampled met-ocean datasets, while spline-based approaches perform better for highly skewed solar irradiance. Preprocessing steps, such as detrending and distribution normalisation, yield only marginal improvements for dense, regularly sampled datasets, and method rankings remain stable under moderate changes in accuracy–speed weightings. Rather than proposing a universal interpolator, this work delivers a reproducible decision-support workflow for temporal resampling of multi-variable met-ocean datasets, supporting early-stage marine renewable energy assessments.