Data Assimilation Algorithms for U.S West and East Coast OFS
Lead Principal Investigator: John Wilkin, Rutgers
Project Overview and Results
This project is directed at enabling NOAA Center for Operational Oceanographic Products and Services (CO-OPS) and IOOS RAs to deliver more accurate and more highly resolved forecasts of water level, velocity, temperature and salinity to key stakeholders concerned with fisheries, ecosystem health, navigation, maritime safety, response to marine environmental hazards, and a sustainable blue economy
NOAA’s West Coast Ocean Forecasting System (WCOFS) and numerous analysis and forecast systems within IOOS RAs are founded on the Regional Ocean Modeling System (ROMS) and its supporting 4-Dimensional Variational (4D-Var) data assimilation (DA) tools. This project will improve, in multiple respects, the performance of these DA systems for operational applications.
Specific objectives of this project are:
- Achieve faster ROMS DA execution time, thereby enabling the practicality of higher resolution analyses and forecasts
- Expand capabilities for better utilizing the information content of high-resolution observations used in existing operational systems
- Create an infrastructure to run coupled ROMS-biogeochemical models that exploit the enhanced ocean physics state estimates delivered by operational DA systems
- Develop IOOS Cloud Sandbox instances of DA systems, including WCOFS, to facilitate hands-on user training in running advanced DA systems and enable the user community to experiment with system performance, ecosystem forecasting, coupling, and observing system design
- Solicit stakeholder priorities and requirements for the next generation of analysis and near real-time forecasts.
Anticipated direct project benefits are (a) the delivery of ROMS data assimilative forecasts at higher resolution than at present by enabling execution of the nonlinear model on a different grid than the 4D-Var iterations, (b) reduced computational footprint of existing operational systems through mixed-precision split executables, (c) greater use of the information content of observations by developing operators that average high-frequency observations, (d) a biogeochemical model coupled to WCOFS output and run operationally within IOOS RAs, (e) archived model configurations and tools in a cloud sandbox to stimulate experimentation and prototyping of system improvements, and (f) communication with stakeholders to obtain feedback on prioritized information needs.
Indirect project benefits of higher resolution and accuracy will cascade to existing downstream products that use operational analysis forecast systems, such as several statistical ecological products that use physical state estimates as inputs such as for HAB prediction and fisheries management.