Sea Level Forecasting Using Wind-Stress Response Functions
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Solution Overview
Problem
Existing methods fail to provide rapid and accurate forecasting of sea level fluctuations during severe weather events like typhoons, leading to inadequate response to storm surges that cause significant loss of life and property.
Innovation Solution
An apparatus and method using a response function to calculate future sea level height based on past wind-induced sea surface stress and sea level data, determining optimal time lags and weighting factors to predict future sea level rise and storm surges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional astronomical tide calculations are used, then tidal height can be predicted with reasonable accuracy, but rapid and accurate forecasting of sea level fluctuations during severe weather events like typhoons cannot be achieved
Solution Approach 1:
The patent transforms the prediction approach by changing from static astronomical tide parameters to dynamic parameters that incorporate wind stress data and time-varying response functions. This allows the system to adapt to severe weather conditions while maintaining prediction accuracy through parameter optimization.
Solution Approach 2:
The patent introduces a response function as an intermediary element that connects wind stress input to sea level height output. This response function acts as a mediator that captures the complex physical relationship between wind forcing and sea level response, enabling accurate storm surge prediction.
2Speed
If a response function with convolution relationship is used to predict future sea level height, then rapid prediction capability is achieved, but determination of optimal time lag and weighting factors increases computational complexity
Solution Approach 1:
The patent applies partial action by focusing computational resources on determining only the critical parameters (optimal time lag and weighting factors) rather than solving the complete physical model. Once these key parameters are optimized, the system can rapidly predict sea level fluctuations without requiring full computational complexity for each prediction.
Solution Approach 2:
The patent performs preliminary action by pre-determining the optimal time lag and weighting factors through calibration using historical data. This preliminary parameter optimization is performed once, and then the same parameters can be reused for rapid predictions during severe weather events, avoiding repeated complex computations.
Data Source
AI summary
An apparatus and method for predicting sea level fluctuations are disclosed. The method for predicting sea level fluctuations includes: acquiring past time-series data of past wind stress and past sea level height, determining an optimal length of a time lag of a response function representing a convolution relationship between the past wind stress and the past sea level height and a future sea level height, determining weighting factors of the past wind stress and the past sea level height in the response function to which the determined optimal length of the time lag is applied, and calculating the future sea level height using the response function to which the determined weighting factors are applied.