Weather Radar Processing With Segmented Cloud Computation
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Solution Overview
Problem
Existing methods for detecting severe weather, such as hail-producing thunderstorms, are limited in accuracy and computational efficiency, leading to uncertainty about potential damage and high costs.
Innovation Solution
A system that ingests radar and numerical weather prediction data to compute weather products, processes them regionally, stitches the regions together, and performs time aggregation to generate a final output solution, utilizing cloud computing resources for parallelization and improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use weather satellites and weather radar imagery to detect hail-producing thunderstorms, then severe weather detection is achieved, but accuracy is limited and computational expense is high
Solution Approach 1:
The patent divides the computational domain into multiple regions and processes radar data in a segmented manner. Different processing methods are applied to different regions based on their characteristics, allowing accurate identification of hail-producing storms while reducing overall computational burden by avoiding uniform high-cost processing across all areas.
2Productivity
If existing methods process radar data to detect severe weather, then weather detection is achieved, but processing speed is slow and time consumption is high
Solution Approach 1:
The patent performs preliminary processing of radar data including collection into radar volumes and storage in storage systems before final analysis. Numerical weather prediction data is pre-processed and stored in databases, allowing the main detection algorithm to work with pre-prepared data structures, significantly reducing processing time during actual severe weather detection events.
3Reliability
If existing methods use comprehensive radar and satellite data analysis, then severe weather detection capability is achieved, but uncertainty about specific parameters like hail probability, size, and duration remains
Solution Approach 1:
The patent introduces numerical weather prediction models as intermediary components that bridge raw radar data and specific severe weather parameter predictions. These models incorporate physical processes and atmospheric conditions to generate reliable estimates of hail probability, size, and duration, transforming basic detection into quantitative prediction with reduced uncertainty.
Data Source
AI summary
Systems and methods for weather radar processing, comprising a processor in communication with a first database and a second database and computer system code executed by the processor. The computer system code causes the processor to ingest radar data from the first database and ingest numerical weather prediction data from the second database. The processor further processes the radar data and the numerical weather prediction data to generate weather products on defined tiles. The processor further processes the weather products on the defined tiles to generate full domain (stitch-tile) data and generates time aggregation data based on the stitch-tile data. The processor further generates a final model using the time aggregation data.