Satellite Mission Planning Using Weather Uncertainty Probabilities
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Solution Overview
Problem
The increasing demand for satellite imagery in Earth observation is hindered by limited satellite resources and the uncertainty of weather forecasts, which leads to inefficient mission planning due to the high computational requirements of ensemble forecasting methods.
Innovation Solution
A computer system and satellite mission plan calculation device that utilize a conditional probability function based on historical meteorological data to identify optimal acquisition zones by calculating the probability of meteorological conditions, allowing for improved mission planning without increasing computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ensemble forecast is used to reduce meteorological uncertainty, then reliability of satellite mission planning is improved, but computing time increases significantly
Solution Approach 1:
The system pre-calculates conditional probability functions for multiple meteorological scenarios and stores them in a database before actual mission planning. When planning is needed, the pre-computed functions are queried and applied directly, avoiding the need to perform full ensemble forecasts at planning time. This resolves the contradiction by performing the computationally intensive work in advance when computing resources are available, while enabling fast mission planning when time is constrained.
Solution Approach 2:
Instead of computing the full ensemble forecast for every possible mission scenario, the system computes only the necessary conditional probability functions for relevant meteorological variables and time periods. The system selectively applies partial ensemble results (specific conditional probabilities for relevant conditions) rather than excessive full ensemble computations for all possible scenarios, optimizing the balance between reliability and computing time.
2Productivity
If more satellite resources are allocated to meet increasing demand for satellite imagery, then productivity is improved, but device complexity increases
Solution Approach 1:
The satellite mission planning system is segmented into independent functional modules: a conditional probability calculation module that processes meteorological data, a database module that stores pre-computed probabilities, and a mission planning module that queries and applies the probabilities. This modular segmentation allows each component to be developed, maintained, and scaled independently, managing system complexity while enabling increased productivity through coordinated operation of multiple satellite resources.
Solution Approach 2:
The conditional probability function database serves multiple purposes: it supports mission planning for different satellites, different imaging scenarios, and various meteorological conditions. The same database infrastructure and probability calculation methods are universally applied across multiple satellite resources, allowing the system to manage increased productivity demands without proportionally increasing complexity, as the core planning logic remains reusable and standardized.
Data Source
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AI summary
The subject of the invention is computer systems (100) for acquiring satellite images. The invention also comprises devices (120) for computing satellite mission plans, and Earth-observation satellites (110). The general principle of the invention is based on the observation that uncertainty related to meteorological conditions may lead to the generation of satellite mission plans the performance of which is poor. Thus, the invention proposes to characterize this uncertainty and to use it to improve the generation of satellite mission plans. In the invention, a model of the uncertainties is constructed on the basis of past meteorological observations and/or predictions. Next, when the plan is generated, the model is exploited, current observations and/or predictions being input into the model to this end. In this way, it is possible to identify acquisition regions in which it is more probable that acceptable images will be acquired at the desired acquisition date.