Multi-source Data Assimilation for Environmental Monitoring
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Solution Overview
Problem
Conventional data assimilation methods face challenges in combining data from multiple sources with varying coverages, accuracies, and resolutions in environmental monitoring, leading to high computational costs and low precision.
Innovation Solution
A method for data assimilation that involves obtaining high-quality data from one region and calibrating lower-quality data from another region based on their relationship in an overlap area, using a fitted curve to unify metrics and units, and extrapolating to create a comprehensive data set with improved accuracy and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground station data is used for environmental monitoring, then measurement precision is improved, but area of coverage deteriorates
Solution Approach 1:
The patent combines multiple data sources (ground stations, ground sensors, laser radars, and satellites) into a unified data assimilation system. By merging these diverse sources with varying coverages and accuracies, the system achieves both high measurement precision from ground stations and large coverage area from satellites and sensors simultaneously.
2Area of stationary object
If conventional data assimilation methods are used to combine multiple data sources, then coverage is improved, but computational cost increases
Solution Approach 1:
The patent transforms the data assimilation process by changing parameters through calibration. It calibrates lower-quality data to match higher-quality data characteristics, and performs extrapolation to extend coverage. This parameter-based approach reduces computational complexity compared to conventional methods while maintaining improved coverage.
3Area of stationary object
If conventional data assimilation methods are used to combine multiple data sources, then coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies parameter changes through calibration processes that adjust lower-quality data to align with higher-quality data characteristics. By calibrating ground sensor data against ground station data and laser radar data against satellite data, the system maintains measurement precision while extending coverage area.
Solution Approach 2:
The patent uses calibration relationships as intermediary mechanisms between different data sources. These calibration functions act as mediators that transfer accuracy characteristics from high-quality data to low-quality data, enabling extended coverage without sacrificing measurement precision.
Data Source
AI summary
The disclosure involves multi-source data assimilation. According to an embodiment, first data associated with an indication of environmental quality in a first region is obtained, and second data associated with an indication of environmental quality in a second region is obtained. The first data is of a higher quality than the second data according to a predetermined criterion. The second data is calibrated according to a relationship between the first and second data in an overlap of the first and second regions. Third data associated with an indication of environmental quality in a third region is determined based on the first data and the calibrated second data, wherein the third region comprises at least the first and second regions.