Dynamic Weather Peril Scoring via Radar and Human Data Blending
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Solution Overview
Problem
Current weather peril scoring systems are inadequate as they rely on outdated, coarse data that doesn't account for minor but damaging events, are biased towards populated areas, and fail to consider the severity of weather events, leading to inaccurate risk assessments for insurance underwriters.
Innovation Solution
A computer-based method and system that integrates human-observed and radar weather data, applies a grid to a geographical region to calculate severity-weighted risk indices, blends these indices, and corrects for biases, providing a dynamic, granular, and accurate peril scoring through a GUI or API.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current peril scoring systems rely on historical catastrophic events, then the scores are based on significant weather impacts, but the scores become stale and do not reflect risks from minor damaging events
Solution Approach 1:
The system transitions from static, infrequently updated peril scores to dynamic, continuously updated scores by integrating real-time radar data and frequent model rejections. This allows the system to capture both catastrophic and minor weather events as they occur, maintaining current and accurate risk assessments without relying solely on historical catastrophic data.
Solution Approach 2:
The system performs preliminary risk assessment by integrating multiple data sources including radar, satellite, and model data before final peril scoring is needed. This proactive approach ensures that scores are always current and reflect the latest weather conditions, rather than waiting for catastrophic events to occur before updating.
2Area of stationary object
If peril scores are provided for large geographic areas, then the data coverage is comprehensive, but the scores lack granularity to predict likelihood at particular locations
Solution Approach 1:
The system divides large geographic areas into smaller grid cells or zones, allowing peril scores to be calculated at multiple spatial resolutions. This segmentation enables both comprehensive regional coverage and location-specific precision, as users can aggregate scores across regions or examine individual high-risk areas in detail.
Solution Approach 2:
The system adds a spatial dimension to peril scoring by implementing multi-resolution gridding, where the same geographic area can be analyzed at different levels of detail. This allows the system to maintain comprehensive coverage while providing granular location-specific predictions when needed.
3Loss of information
If peril scoring relies on human observational data, then the data reflects actual weather events, but the scores are biased towards populated areas with more observations
Solution Approach 1:
The system combines multiple data sources including human observations, radar data, satellite imagery, and numerical weather model data. This multi-source integration compensates for the limitations of any single source, particularly reducing population bias by weighting radar and model data that provide uniform coverage regardless of human presence.
Solution Approach 2:
The system applies bias correction factors and weighting parameters to adjust for population density effects in human observational data. By dynamically adjusting these parameters based on location characteristics, the system maintains the value of human observations while eliminating the population bias that would otherwise skew peril scores.
4Reliability
If peril scores focus on probability of occurrence, then the scores indicate likelihood of weather events, but they fail to consider the severity of events
Solution Approach 1:
The system creates a composite peril score that integrates both probability and severity components. Rather than relying on a single metric, the system combines multiple parameters including event likelihood, expected intensity, potential damage levels, and historical severity patterns to produce a comprehensive risk assessment that reflects both how likely and how severe weather events are expected to be.
Data Source
AI summary
A computer-based method of formulating and delivering dynamic, severity-based weather peril scoring includes ingesting human-observed weather data and radar weather data, applying a grid that has a plurality of cells to a map of a geographical region to divide the geographical region into a plurality of areas, with each area being defined by a corresponding one of the grid cells, for each area/grid cell: calculating, with a computer-based processor, a first severity-weighted risk index for at least one weather peril based on the human-observed weather data, calculating, with the computer-based processor, a second severity-weighted risk index for the at least one weather peril based on the radar weather data, and blending, with the computer-based processor, the first severity-weighted risk index and the second severity-weighted risk index to produce a blended risk index.