A Multi-Resolution Probabilistic Ocean Current Forecasting System for Offshore Energy Operations

Abstract

Abstract We present a multi-resolution probabilistic ocean forecasting system designed to support offshore energy operations worldwide. The system is composed of an integrated suite of ocean circulation models including a global model of 25 km resolution, Atlantic and Indian Ocean models of 6 km resolution, and 8 fine scale models (< 3 km resolution) for high-priority regions such as the Gulf of Mexico, offshore Brazil, West Africa, and the Caribbean. It expands on current ocean prediction systems through exclusive access to proprietary data streams, state-of-the-art data assimilation methods and ensemble modeling capabilities. The global and basin scale models incorporate large-scale information from satellite and in-situ observations and provide initial and boundary conditions for the regional models. The regional scale models then dynamically downscale the initial and boundary conditions to produce both deterministic and probabilistic fine-scale forecasts for regions of interest. The system has been validated for consistency, quality, and accuracy against a suite of publicly available models and field observations (Remotely sensed observations, ARGO float data and Drifting Buoys). Results show that the system compares favorably to other leading ocean current forecasting systems such as HYCOM and the Mercator system. Experiments in the Gulf of Mexico demonstrate that the fine-scale forecasts are further improved by using proprietary data streams and when operated in an ensemble (probabilistic) mode they also provide robust quantification of uncertainty in forecasts. The system has been operational since the beginning of 2017, providing daily 7-day forecasts of ocean currents. Additionally, 20-year hindcasts for some regions are in progress and outputs will be available for extreme value analysis for planning and structural design. It is expected that Ocean current hindcasts and forecasts through the new system will translate to increased situational awareness, reduced downtime, improved environmental protection, and safety.