MMPI Framework for 3D Ocean-Aware Cyclone Intensity Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting tropical cyclone intensity, such as Emanuel's Potential Intensity (EMPI), do not adequately account for subsurface ocean information like ocean heat content (OHC) and dynamic ocean processes, leading to overestimations and limited granularity in forecasting.
Innovation Solution
The Moored Maximum Potential Intensity (MMPI) framework incorporates three-dimensional ocean temperature and velocity data, dynamic ocean processes, and radiative heat fluxes to refine the prediction of tropical cyclone intensity using a machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Emanuel's Potential Intensity (EMPI) method is used, then the calculation is simple with basic parameters, but the prediction accuracy is limited due to lack of subsurface ocean information
Solution Approach 1:
The patent transitions from two-dimensional surface data (SST, surface winds) to three-dimensional ocean data by incorporating subsurface temperature profiles and velocity fields. This dimensional expansion allows the system to capture vertical ocean structure and dynamic processes that control heat fluxes, thereby improving MPI prediction accuracy while accepting increased data complexity.
Solution Approach 2:
The patent introduces dynamic ocean processes (advection, diffusion, mixing) as intermediary mechanisms that link subsurface ocean conditions to surface heat fluxes. These processes act as mediators that translate complex three-dimensional ocean state into meaningful constraints on tropical cyclone intensity, bridging the gap between detailed ocean data and MPI predictions.
2Measurement precision
If subsurface ocean data and dynamic processes are incorporated, then the forecasting accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent extracts and isolates the key dynamic ocean processes (advection, diffusion, mixing) from the full three-dimensional ocean model. By identifying and extracting only the essential processes that control heat flux variability, the system achieves improved forecasting accuracy while reducing computational complexity compared to running complete ocean general circulation models.
Solution Approach 2:
The patent changes the parameterization approach by representing dynamic ocean processes through simplified diagnostic relationships rather than full prognostic models. This allows subsurface ocean effects to be incorporated into MPI calculations through modified heat flux parameterizations, improving accuracy without the full computational burden of dynamic ocean modeling.
3Loss of information
If three-dimensional ocean data is used, then the understanding of ocean controls on TC intensity improves, but the data processing difficulty increases
Solution Approach 1:
The patent performs preliminary processing of three-dimensional ocean data by pre-calculating ocean heat content, mixed layer depth, and other integrated metrics from subsurface profiles. This preliminary action transforms raw three-dimensional data into condensed diagnostic fields that are easier to process and directly relevant to MPI calculations, reducing the burden of handling full 3-D datasets.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The MMPI framework provides more accurate and realistic predictions of tropical cyclone intensity by explicitly considering ocean dynamics, reducing overestimations and enhancing forecasting capabilities.
Implementation Method 1
determining based on one or more dynamic ocean processes a set of heat fluxes associated with ocean heat
Implementation Method 2
identifying a first set of data comprising a three-dimensional (3-D) field of ocean temperature and velocity
Implementation Method 3
determining based on one or more dynamic ocean processes a set of heat fluxes associated with ocean heat
Implementation Method 4
identifying a second set of data comprising a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation
Data Source
AI summary
A method of forecasting a maximum wind intensity associated with tropical cyclones, the method includes identifying a three-dimensional (3-D) field of ocean temperature and velocity, identifying a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation, determining a set of heat fluxes associated with ocean heat, and generating a 2-D map of the maximum potential intensity (MPI) based on (i) the set of heat fluxes and (ii) the first and second sets of data. The method may include training a machine learning model based on the first and second sets of data or the 2-D map of the MPI, and performing, based on the trained machine learning model and the 2-D map of the MPI, a mitigating activity corresponding to anticipated effects associated with the determined upper bound for tropical cyclone wind speed at a geographical location.