Ocean-Acoustic Data Assimilation via Coupled Models
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Solution Overview
Problem
Current ocean forecasting methods, such as acoustic tomography and variational retrievals, face challenges including computational intensity, inaccurate results, and double-assimilation errors in ocean model analysis, particularly when using acoustic pressure observations.
Innovation Solution
The integration of advanced data assimilation systems that utilize weighted least-squares minimization and the coupling of forward and adjoint acoustic models with the Navy Coastal Ocean Model (NCOM) 4DVAR and 3DVAR systems to directly correct ocean models with acoustic pressure observations, reducing computational burden and error impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic tomography is used to measure ocean properties by inverting environmental sound speed, then measurement capability is provided, but computational intensity increases and measurement precision deteriorates
Solution Approach 1:
The patent replaces the traditional mechanical inversion process of acoustic tomography with a data assimilation system that uses variational methods. Instead of computationally intensive inversion algorithms that minimize differences between modeled and measured travel times, the system uses a variational retrieval approach that assimilates acoustic pressure observations into an ocean model framework, significantly reducing computational burden while maintaining or improving measurement precision.
Solution Approach 2:
The patent changes the fundamental parameter being measured and assimilated. Rather than directly inverting for sound speed using travel time differences, the system assimilates acoustic pressure observations and uses the ocean model to retrieve sound speed and other ocean properties. This parameter transformation enables more efficient computation while improving the physical consistency of the results through the ocean model framework.
2Measurement precision
If variational retrievals are used to assimilate acoustic pressure observations, then measurement precision improves, but device complexity increases due to double-assimilation processes
Solution Approach 1:
The patent extracts and eliminates the redundant assimilation step from the traditional variational retrieval process. Instead of performing separate retrieval and assimilation operations that constitute double-assimilation, the system directly assimilates acoustic pressure observations into the ocean model in a single integrated process, removing the unnecessary intermediate retrieval step and its associated complexity.
Solution Approach 2:
The patent merges the retrieval and assimilation processes into a unified data assimilation system. The acoustic pressure observations are directly incorporated into the ocean model analysis through a combined variational approach, eliminating the need for separate retrieval and assimilation operations. This merging reduces system complexity while maintaining the ability to produce accurate ocean state estimates.
3Ease of operation
If neural network-based observation operators are used in 3DVAR assimilation systems, then ease of operation improves, but measurement precision deteriorates due to training limitations and vulnerability to errors
Solution Approach 1:
The patent replaces the neural network-based observation operator with a physics-based variational approach. Instead of relying on trained neural networks that have limitations in generalizing to unseen conditions, the system uses a variational retrieval method grounded in physical principles and the ocean model framework. This substitution eliminates training limitations and improves precision while maintaining operational simplicity through the unified assimilation approach.
Data Source
AI summary
A method of determining ocean state. The method may include receiving, by a processing device, data associated with a prior ocean forecast state, and receiving, by the processing device, data associated with a first set of ocean temperature and salinity observations. The method may include receiving, by the processing device, data associated with a first set of ocean acoustic pressure observations. The method may include determining, by the processing device, a correction to the prior ocean forecast state based on a forward acoustic model, on an adjoint acoustic model, on the data associated with a first set of ocean temperature and ocean salinity observations, and on the data associated with a first set of ocean acoustic pressure observations, and generating, by the processing device, a current ocean state based on the determined correction.