Brazilian Margin Wave Energy and Bimodality Dataset (BMWEB)

The dataset compiles oceanographic and energy parameters calculated to characterize wave energy potential and bimodal spectral structure along the Brazilian continental margin. Its development is based on monthly mean climatologies from the WAVERYS product and the decomposition of the wave spectrum into three partitions: locally wind-generated waves (WW), primary swell (SW1), and secondary swell (SW2). Input variables include the significant wave height, mean period, and mean direction for each partition, as well as the total significant wave height. Directions follow the meteorological convention—indicating the direction from which the waves originate. Additionally, GEBCO 2026 bathymetry, remapped to the WAVERYS grid, was incorporated to estimate ocean depth and account for depth-related effects on wave propagation. The dataset includes energy density and power flux for the total wave field and the WW, SW1, and SW2 partitions, calculated using both the deep-water approximation and a depth correction based on the linear dispersion relation, wavenumber, and group velocity. It also contains indicators of energy closure between the partitions and the total wave field, relative energy residuals, and partial closure values for each pair of components. These parameters allow us to evaluate whether the energy reconstructed from WW, SW1 and SW2 adequately reproduces the total energy and to detect possible deficits, overestimations or inconsistencies associated with spectral partitioning and the use of monthly climatological averages. To describe the resource organization, the relative contributions of each partition to energy density and power flux were calculated, as well as the energy-weighted dominant direction using circular statistics. Directional complexity is represented by the minimum angular separation between WW–SW1, WW–SW2, and SW1–SW2. From these metrics, the energy bimodality index (BI)—which quantifies the energy balance between two systems—and the dominant (or energy-directional) bimodality index (DBI)—which combines energy distribution with angular separation to identify the predominant bimodal pair in each cell and month—were derived. Finally, the dataset incorporates a monthly classification of spectral states into unimodal, bimodal, or multimodal categories, alongside two integrated climatological indicators: bimodal persistence (BP)—expressed as the number of months dominated by bimodal or multimodal conditions—and the bimodal relevance index (BRI), which weights bimodality by the magnitude of total energy. Together, these parameters make it possible to distinguish regions with high or low energy availability and varying degrees of spectral-directional complexity, providing a basis for simultaneously evaluating the magnitude, stability, persistence, and physical quality of the wave energy resource.