Project research activities are organized into tasks, with teams of academic, industry, and government partners.


  Description Team Objectives and Progress To Date
Objective 1:  Development of forecast systems 
Task 1.1 Enhance the Navy forecast systems with a) increased horizontal and vertical resolution, b) coupling with an active atmosphere and wave models, and c) new DA capabilities and assimilation of new data sources (including UGOS-3 observations).  E. Chassignet, S. Morey, G. Jacobs (NRL), A. Wallcraft, A. Bozec, D. Dukhovskoy, O. Zavala-Romero, S. Bulusu, A. Srinivasan, P. Miron, a research scientist hosted at NRL, and a graduate student working closely with NRL link
Task 1.2 Develop a Gulf of Mexico analysis-forecast system based on ROMS (coupled and uncoupled) and 4-dimensional variational and ensemble Kalman Filter DA with the goal of transitioning the system to the NOAA NOS/IOOS Regional Associations (RAs) that serve the Gulf of Mexico. Augment the ROMS DA system by applying and developing new eXplainable Artificial Intelligence (XAI) and ML techniques to i) improve the fidelity and computational efficiency of the DA system, ii) extract information from observations and model output, and iii) post-process system output for feature detection and extreme event prediction.  R. He and A. Moore, P. Burke (NOAA), D. Snowden (NOAA), and S. Pe'eri (NOAA) link
Task 1.3 Increase and enhance the capabilities of the open-source T-SIS used by industry for providing operational guidance and forecasts. A. Srinivasan, E. Chassignet, A. Bozec, and D. Dukhovskoy link
Task 1.4 Apply OSSEs to 1) quantify the utility of future observing systems (Surface Water Ocean Topography (SWOT) altimetry, ROCIS, UGOS-3 collaborators’ observations) and 2) determine optimal and rapid adaptive sampling strategies. B. Cornuelle, A. Moore, R. He, J. Sheinbaum, G. Gopalakrishnan, S. Bulusu, D. Dukhovskoy, E. Chassignet, and S. Morey in collaboration with NRL and NOAA link
Task 1.5 Assess the value of industry ROCIS and AXBT observations to model forecast skill through Observing System Experiments (OSEs) and real-time in-situ experiments assimilating adaptively sampled surface current data from the airborne ROCIS system, temperature profile data from AXBTs, and other UGOS collaborators’ data as available.  S. Morey, E. Chassignet, G. Stuart, B. Williams, P. Barros, and J. Stear (Chevron) link
Objective 2:  Development of forecasting tools and actionable knowledge for full-water column currents
Task 2.1 Apply ML methods (e.g., neural networks) to develop analyses of dynamical fields associated with the LC and eddies from satellite ocean color for operational use and for assimilation in forecast models. D. Dukhovskoy, O. Zavala-Romero, P. Lermusiaux, S. Bulusu, and  Kate Seikel, in collaboration with NOAA and C. B. Trott of NRL link
Task 2.2 Develop and apply probabilistic forecast and analysis techniques to quantify variability and uncertainty due to initial and boundary conditions, predictability limits, predictive capabilities, and operational risks. Apply Bayesian and other ML methods to estimate and predict ocean variables from observations and to improve existing ocean modeling systems. P. Lermusiaux, O. Zavala-Romero, D. Dukhovskoy, E. Chassignet, B. Cornuelle, and R. He in collaboration with NRL and NOAA link
Task 2.3 Apply improved methods of forecasting deep currents associated with TRWs and other features based on their linkages to upper ocean dynamics. S. Morey, D. Dukhovskoy, and J. Stear link
Objective 3: Application of improved Gulf of Mexico forecast models to hurricane forecasting and fisheries
Task 3.1 Quantify the impact of specific observations on hurricane intensity and sea state forecast error and implement new DA capabilities in next-generation modeling tools developed by NOAA’s Environmental Modeling Center (EMC) for hurricane forecasting.  M. Le Hénaff, H-S. Kim (NOAA), A. Mehra (NOAA), E. Chassignet, A. Bozec, and A. Wallcraft link
Task 3.2 Apply advances in Gulf of Mexico prediction and reanalyses to NOAA fisheries recruitment forecasting and dynamic management capabilities. M. Karnauskas (NOAA), S. Morey, E. Chassignet, and a graduate student link