Mobile Weather Data Aggregation for Sparse Road Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile observations from consumer-quality sensors have low quality and lack time-series data, making them difficult to integrate into weather models, especially for improving road weather information, and existing systems like RWIS are sparse and costly.
Innovation Solution
A system and method that aggregates and processes mobile data from non-stationary sensors to generate virtual observations, adjusting unified weather model estimates using spatiotemporal bins and quality control, enabling accurate weather prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mobile observations from consumer-quality sensors are used, then the quantity of observations increases and coverage expands, but the quality and reliability of the observations deteriorate
Solution Approach 1:
The patent combines multiple mobile observations from different consumer-quality sensors into aggregated datasets. By merging observations from multiple sources, the system compensates for individual sensor deficiencies and achieves improved overall data quality while maintaining high quantity and broad coverage.
Solution Approach 2:
The patent introduces weather models as an intermediary component that processes and adjusts mobile observations. The weather models act as a mediator that transforms raw mobile sensor data into refined weather information, bridging the gap between low-quality mobile observations and reliable weather predictions.
2Area of stationary object
If mobile observations are used, then spatial coverage increases, but the availability of time-series data at fixed locations decreases
Solution Approach 1:
The patent embraces the dynamic nature of mobile observations by designing a system that processes data from moving sensors. Rather than requiring fixed locations, the system dynamically adjusts to capture weather conditions across varying spatial positions, transforming the mobility constraint into a coverage advantage.
Solution Approach 2:
The patent transitions from the traditional stationary spatial dimension to a spatiotemporal dimension by incorporating time as a critical factor. Mobile observations are processed with temporal context, allowing the system to analyze weather patterns across both space and time, effectively converting the lack of fixed-location time series into a spatiotemporal analysis advantage.
3Adaptability or versatility
If mobile observations are integrated into weather models, then weather information coverage improves, but the complexity of data processing increases
Solution Approach 1:
The patent employs weather models as an intermediary layer that simplifies the integration of mobile observations. The models serve as a buffer that handles the complexity of processing mobile data, transforming raw observations into standardized weather information that can be easily integrated into existing weather forecasting systems.
Solution Approach 2:
The patent adjusts processing parameters and methods based on the characteristics of mobile observations. By dynamically changing processing parameters such as aggregation levels, quality thresholds, and model adjustment factors, the system manages processing complexity while maintaining comprehensive weather information coverage.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system (100) for using mobile data to improve weather information is provided. The system (100) includes a weather prediction station (120) configured to receive stationary observation data provided by a plurality of stationary weather stations (110) along with data from a plurality of input weather models (115) and generate unified weather model estimates based on the stationary observation data, the input weather model data, and a processor (130). The processor (130) is configured to aggregate mobile observation data provided by a plurality of non-stationary sensors (140) and use the aggregated mobile observation data to adjust the weather model estimates.