Lagrangian and Coupled Data Assimilation enhanced by Machine Learning to improve Operational Ocean Prediction
Abstract
The objective of this project is to build an ensemble-based ocean data assimilation (DA) system that can effectively assimilate observation data relevant to constraining a high-resolution analysis of the ocean surface, with computational costs comparable to the existing 3D-Var NCODA system used by the US Navy. To achieve this objective, two guiding principles are to use innovative new approaches to: (1) utilize new observational data types that are typically ignored for operational ocean data assimilation, and (2) reduce the computational costs of the cycled ocean data assimilation process. We propose a DA system that: (a) uses Lagrangian data assimilation (LaDA) to utilize high-resolution satellite imagery and surface drifters, (b) applies strongly coupled dataassimilation (SCDA) to utilize near-surface atmospheric data simultaneously with in situ and satellite ocean measurements, and (c) applies machine learning (ML) proxy models to produce ensemble-based forecast error covariance estimates at reduced computational cost. New observing platforms are producing ever more data that need to be assimilated, including highresolution SST and ocean color images from VIIRS, altimetry (at upcoming SWOT resolutions), and SAR images. These can and should be incorporated into the data assimilation process, with reasonable computational costs.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2019
- Source ID
- N000141912522
Entities
People
- Stephen G Penny
Organizations
- Office of Naval Research
- United States Navy
- University of Maryland