Metocean-stats v1.2: An open-source framework for reproducible and actionable metocean data analysis

Abstract

Metocean analyses are fundamental to offshore and coastal engineering, maritime operations, and renewable energy development, where reliable estimates of winds, waves, currents, sea level, and related variables are required for structural design, safety assessment, and operational planning. Although large metocean datasets are increasingly available, analyses are often performed using ad hoc scripts and fragmented workflows, limiting reproducibility, transparency, and efficient knowledge transfer. We present metocean-stats , an open-source computational framework designed to improve the usability of metocean datasets for engineering, operational, and research applications. By unifying statistical characterisation, extreme value analysis, spectral analysis, weather-window assessment, and dataset verification in a reproducible Python workflow, the framework converts long metocean time series into site-specific information that can directly inform engineering and operational decisions. The tool operates directly on Pandas DataFrames and integrates a broad range of engineering-relevant methodologies within a unified workflow. The framework is demonstrated using long-term hindcast and reanalysis datasets for the Norwegian Sea. By consolidating widely used metocean analysis techniques into a transparent and extensible open-source environment, metocean-stats facilitates reproducible research, supports engineering design studies, and the framework aims to reduce the gap between methodological developments in metocean statistics and their routine application in engineering practice.