Sea Level Response Function for Fast Storm Surge Forecasting
Find Innovative SolutionsGenerate Solutions
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 utilizing 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
1Reliability
If astronomical tide calculations are used to predict sea level, then prediction accuracy is maintained for normal tidal patterns, but the system fails to accurately forecast storm surge events during severe weather
Solution Approach 1:
The invention transitions from using only astronomical tide parameters to incorporating wind stress parameters and their temporal relationships. By changing the predictive parameters to include wind stress time-series data and response time lags, the system adapts to severe weather conditions while maintaining reliability in normal conditions.
Solution Approach 2:
The invention introduces dynamic response time lags that capture the temporal relationship between wind stress and sea level changes. This dynamic approach allows the system to adapt to varying weather conditions, making the prediction method versatile for both normal tidal patterns and extreme storm surge events.
2Productivity
If traditional tide prediction methods are used, then computational simplicity is maintained, but rapid forecasting capability during typhoons is insufficient
Solution Approach 1:
The invention replaces traditional mechanical tide prediction methods with a computational approach using response functions and convolution operations. This substitution enables rapid processing of wind stress time-series data while maintaining high prediction accuracy for storm surge events.
3Measurement precision
If response function with convolution relationship is used to predict future sea level, then prediction accuracy for storm surges is improved, but computational complexity increases
Solution Approach 1:
The invention extracts the essential convolution relationship between wind stress and sea level changes into a dedicated response function. By isolating this critical computational element, the system achieves high prediction accuracy while managing complexity through focused mathematical modeling rather than comprehensive complex systems.
Data Source
Figure 1~2
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.