Methodology: Using Storm Clustering to Identify Large Scale Events

Identification of storm events in historical metocean data is a critical part of the derivation of return period extremes and in the understanding of harsh conditions experienced offshore. Peak conditions by variable (wind speed, wave height, current speed, etc.) stratified by forcing conditions (tropical, extra-tropical, monsoonal, etc.) and by direction are all typical requirements for operations and design of offshore structures. In order to develop design-level metocean hindcasts, Oceanweather faces the challenging task of reviewing and reanalyzing events over a large spatial and temporal domain to ensure peak conditions are well resolved and unbiased. To meet this challenge, a new storm clustering technique has been developed and applied in the latest Joint Industry Project: Brazil Offshore Meteorological and Oceanographic Study (BOMOS-2).

2019 Year Updates to GFEC, GFC-2, and GROW2012

Oceanweather are pleased to announce that the GROW-FINE hindcasts East Coast (GFEC) and Caribbean (GFC-2) are now updated through 2019. For GFC-2 this includes the 2019 storms Dorian, Jerry, and Karen. For GFEC this includes Dorian as well as two (2) additional extratropical events in 2019.

GROW2012 is also updated through 2019.

Phase 1 of BOMOS-2 Joint Industry Project Released

Oceanweather Inc. (OWI) is pleased to announce the completion of Phase 1 of the BOMOS-2 JIP (Brazil Offshore Meteorological and Oceanographic Study Joint Industry Project).

BOMOS-2 is a comprehensive study of the wind and wave climate offshore Brazil improving upon the recent global ERA5 reanalysis in which extreme wind and wave events may not be well represented. This phase of the study consists of a hindcast for the continuous time period from 2000 – 2018, archived at a 30-minute time step and incorporating the expert wind-field analysis of over 70 storms using OWI’s proven IOKA approach. Storms were selected to be analyzed based on insitu and remote-sensed measurements, as well as with OWI’s novel peak identification and clustering methodology (used for the first time in this study).