Parametric Storm Risk Grid Using Distributed Wind Sensors
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Solution Overview
Problem
Current systems fail to accurately measure and forecast storm risks and impacts, particularly for tropical storms, due to limitations in predicting wind field dynamics and localized damage, leading to inadequate risk assessment and insurance coverage.
Innovation Solution
An automated system utilizing geographically distributed weather stations to measure wind speeds and generate high-resolution wind field maps, which are used to calculate storm risk exposure and trigger insurance payments based on predefined indices, providing a more dynamic and localized view of storm impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional storm rating systems are used, then general storm risk assessment is provided, but localized precision and measurement accuracy of storm impacts are insufficient
Solution Approach 1:
The patent divides the geographical area into discrete grid cells, with each cell having specific storm exposure parameters. This segmentation allows localized measurement of storm impacts at high resolution while using standardized processing methods for each cell, balancing precision with systematic manageability.
Solution Approach 2:
The patent introduces a spatial grid dimension overlaying the geographical area, transforming continuous storm exposure assessment into discrete grid-cell-based measurements. This dimensional approach enables precise localization of storm impacts across the landscape while maintaining systematic data structure.
2Productivity
If automated parametric risk-transfer systems are implemented, then rapid insurance disbursement is achieved, but accurate location-dependent storm risk recognition is required
Solution Approach 1:
The system pre-calculates and stores storm exposure parameters for each grid cell before storm events occur. When a storm happens, the system rapidly matches observed storm parameters against pre-computed exposure data to determine insurance payouts, achieving both speed and accuracy without real-time complex calculations.
Solution Approach 2:
The patent creates detailed digital representations (copies) of physical storm exposure characteristics for each grid cell, including building inventories, vulnerability factors, and exposure metrics. These digital copies enable rapid automated assessment by comparing observed storm parameters against the stored digital twins of the landscape.
3Measurement precision
If high-resolution wind field data is collected across the geographical area, then localized storm impact precision is improved, but data quantity and processing complexity increase
Solution Approach 1:
The patent segments the continuous wind field data into discrete grid cell measurements, where each cell contains aggregated wind exposure parameters. This segmentation reduces data volume by representing spatially distributed wind fields as manageable grid-based datasets while preserving localized variation information.
Solution Approach 2:
The system assigns specific wind exposure characteristics to each grid cell based on local conditions such as topography, land use, and building density. This local quality approach ensures high-resolution localized measurement where needed while avoiding uniform high-resolution data collection across entire regions, optimizing data efficiency.
Data Source
AI summary
Proposed is a system and a method for a parametric risk transfer system based on automated location-dependent probabilistic tropical storm risk and storm impact forecast, wherein weather measuring parameters of weather events are measured by means of a plurality of delocalized distributed weather flow stations and transmitted to a central system, and wherein the measured weather measuring parameters at least comprise measuring parameters of wind speed and/or maximum wind speed within a predefined time frame. A spatial high-resolution grid comprising grid cells is generated over a geographical area of interest, and a wind field profile is dynamically generated, wherein by triggering an indexed wind field parameter of the wind field profile exceeding a predefined trigger, an output activation signal is generated based on the wind field parameter and transmitted to an associated activation device, wherein the operation of the activation device is steered by the transmitted output activation signal.