Ocean Temperature Salinity Correction via Sea Surface Height

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Solution Overview

Problem

Current methods for correcting ocean forecast models using sea surface height (SSH) data are inefficient and prone to representativeness errors, often leading to model self-confirmation effects and inability to directly assimilate satellite SSH data for improving temperature and salinity structure correlations.

Innovation Solution

A system that precomputes relations between temperature, salinity, and geopotential using historical observations, allowing for efficient cross-correlation and direct assimilation of SSH data to correct ocean forecast models, thereby preventing model drift and enhancing predictive accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data assimilation through construction of synthetic ocean profiles is used to correct ocean models using SSH, then model correction is enabled, but representativeness errors are introduced and information from profile observations is dampened

Engineering Contradiction:

Improvemodel correction accuracyVSAvoidprofile observation information

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary relationship through precomputed cross-correlation matrices that link SSH observations to subsurface T/S properties. Instead of directly constructing synthetic profiles that may introduce errors, the system uses historical observations to establish statistical relationships (covariance matrices) that act as intermediaries, allowing SSH data to indirectly correct model fields while preserving the integrity of actual profile observations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary computation of cross-correlation matrices and covariance relationships between SSH and subsurface properties using historical data before actual model correction. This precomputation phase establishes accurate statistical relationships that can be applied during operational forecasting, avoiding the need to construct synthetic profiles in real-time and thereby preventing representativeness errors.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If data assimilation through model-derived error covariances is used, then SSH data can be assimilated, but model self-confirmation effects occur

Engineering Contradiction:

ImproveSSH data assimilation efficiencyVSAvoidmodel independence

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent uses precomputed cross-correlation matrices derived from historical observations as intermediaries between SSH data and model correction. These externally derived statistical relationships act as independent mediators that connect surface observations to subsurface properties without relying on the model's own error covariances, thereby avoiding self-confirmation effects while maintaining efficient data assimilation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If velocity observations are used to correct ocean models, then existing historical data can be utilized, but the method cannot directly assimilate SSH data from satellites

Engineering Contradiction:

Improvedata source compatibilityVSAvoidSSH data assimilation capability

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent extends the applicability of historical observation data to multiple purposes: the same historical T/S profiles used previously for velocity corrections are now also used to compute cross-correlation matrices for SSH data assimilation. This multi-functional use of historical data enables the system to handle both velocity and SSH observations through a unified framework, directly assimilating satellite SSH data while maintaining compatibility with existing data sources.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If computational efficiency is prioritized in model correction, then existing super-computer systems can be used, but representativeness errors increase

Engineering Contradiction:

Improvecomputational efficiencyVSAvoidcorrelation accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs computationally intensive tasks in advance by precomputing cross-correlation matrices and covariance relationships using historical observations. These precomputed statistical relationships are stored and reused during operational forecasting, eliminating the need for repeated complex calculations while maintaining high accuracy. This approach enables direct SSH data assimilation with full representativeness without compromising computational efficiency.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10268783B2System and method for correcting a model-derived vertical structure of ocean temperature and ocean salinity based on sea surface height observations

Publication Date: 2019.04.23 THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY

AI summary

System and method for correcting the vertical structure of the ocean temperature and salinity can enable the use of sea surface height (SSH) measurements to correct ocean forecast models. In the present embodiment, three relations that can be precomputed are exploited: (1) the relation between temperature and salinity throughout a water column, (2) the relation between temperature/salinity and geopotential, and (3) the relation between geopotential and SSH. The relations are stored in a form that allows efficient application through a cross-correlation matrix.