LEO Satellite Constellation Forecasting for Real-Time Space Weather
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Solution Overview
Problem
Existing space weather forecasting systems suffer from timeliness and resolution issues, with slow data integration processes that hinder real-time analysis and delay responses to sudden changes, leading to potential damage from space weather events.
Innovation Solution
A computer-implemented method and apparatus that integrate internal data from Low Earth Orbit satellites and external ground-based data, computing secondary key indices from primary indices, adjusting them based on computed secondary indices, and extrapolating to predict future space weather events, using a centralized data repository and processing modules to enhance accuracy and timeliness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data integration processes are used to combine satellite and ground-based data, then forecast accuracy is improved, but processing time increases causing delays in real-time analysis
Solution Approach 1:
The data integration process is segmented into modular components: data ingestion from multiple sources, primary index computation, secondary index derivation, and forecast generation. Each module operates independently and can be processed in parallel, reducing overall processing time while maintaining comprehensive data analysis for accurate forecasts.
Solution Approach 2:
Primary key indices are computed in advance from raw satellite and ground-based data, and stored in a optimized format. When forecasting is needed, these pre-processed indices are readily available, eliminating the need for real-time computation of basic metrics and allowing faster secondary analysis and prediction.
2Reliability
If comprehensive data from multiple sources is collected, then forecast reliability is improved, but system complexity increases
Solution Approach 1:
The system employs a universal data processing framework that handles multiple data types (satellite observations, ground-based measurements, model outputs) through a common architecture. The same computational engines process different data sources, and a unified index system integrates all inputs, reducing the need for separate processing pipelines for each data source.
Solution Approach 2:
Key indices serve as intermediary variables that simplify the integration of complex multi-source data. Instead of directly combining raw data from satellites, ground stations, and models, the system computes standardized primary indices from each source, then derives secondary indices that capture relationships between sources, finally producing forecasts from these synthesized indicators.
3Speed
If real-time processing is implemented, then response time to space weather changes is improved, but computational resource requirements increase
Solution Approach 1:
The system applies different processing intensities to different data streams based on their urgency and importance. Critical real-time parameters from satellite observations receive immediate processing with high computational priority, while less time-sensitive ground-based data are processed at lower intensity. This localized quality approach ensures rapid response to critical events while conserving computational resources for non-urgent data.
Data Source
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AI summary
This disclosure pertains to a space weather forecast system (1), comprising a space weather forecast apparatus (100), a plurality of the LEO satellites (202), and one or more ground stations (204) connected to the space weather forecast apparatus (100), the one or more ground stations (204) being further configured to send first electromagnetic signals to the LEO satellites (202), to receive second electromagnetic signals from the LEO satellites (202) and to transmit the received second electromagnetic signals to the space weather forecast apparatus (100).