Surrogate Modeling for High-Resolution Gas Emission Distribution
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Solution Overview
Problem
Existing methods for estimating gas emission distribution, such as bottom-up and top-down approaches, face challenges in accuracy, computational cost, and data coverage, particularly in remote areas, and require complex mathematical formulations and deep understanding of atmospheric transport models.
Innovation Solution
A dual optimization scheme using a surrogate model that trains on numerical transport models and corrects prior emissions by minimizing the difference between predicted and satellite concentration data, allowing for high-resolution emission estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If top-down methods using data assimilation algorithms (EnKF, 4D-Var) are used to estimate emission distribution, then measurement precision and global coverage are improved, but device complexity and computational cost increase significantly
Solution Approach 1:
The patent introduces a pre-trained neural network as an intermediary between satellite concentration measurements and emission distribution estimates. This neural network surrogate model replaces the complex data assimilation algorithms (EnKF, 4D-Var), providing a computationally efficient alternative that maintains accuracy while dramatically reducing algorithmic complexity and computational resources required.
Solution Approach 2:
The patent creates a simplified copy or approximation of the complex atmospheric transport and data assimilation system using a neural network. This neural network is trained offline on synthetic data generated from transport models, then deployed as a lightweight surrogate that replicates the functionality of complex algorithms without their computational burden, enabling rapid emission estimation.
2Manufacturing precision
If high-resolution emission estimation is performed using traditional methods, then manufacturing precision is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of the neural network offline using synthetic data generated from atmospheric transport models. This pre-computation phase creates a ready-to-use surrogate model that can rapidly process satellite measurements without requiring complex real-time computations, thus achieving high-resolution emission estimates quickly when deployed.
Solution Approach 2:
The neural network serves as a computational copy that replicates the behavior of complex atmospheric transport models but executes much faster. This surrogate model enables high-resolution emission estimation by approximating the physics-based models in a computationally efficient manner, reducing computation time while maintaining precision.
3Reliability
If bottom-up methods are used to gather detailed source data, then reliability of emission factors is improved, but device complexity and data collection requirements increase
Solution Approach 1:
The patent employs a universal neural network model that can process satellite concentration measurements to estimate emissions from diverse sources without requiring source-specific data collection systems. This single model handles multiple emission types and sources, replacing the complex, source-by-source data gathering infrastructure required by bottom-up methods while maintaining reliability through its training on comprehensive synthetic data.
4Measurement precision
If complex data assimilation algorithms are implemented, then measurement precision is improved, but ease of operation decreases due to required expertise
Solution Approach 1:
The patent replaces complex data assimilation algorithms with a pre-trained neural network surrogate that is much easier to operate. The neural network encapsulates the complex mathematical formulations and atmospheric physics knowledge, presenting a simple interface that requires minimal expertise to deploy while maintaining the measurement precision of the original complex algorithms.
Data Source
AI summary
Obtaining accurate estimates of emission distribution of gases is a technically challenging as well as environmentally relevant problem. Existing techniques have high computational cost, need deep mathematical and computational expertise, and are prone to errors. Hence, embodiments of present disclosure provide a method and system for determining emission distribution using a surrogate model. The method takes prior emission distributions of gas and updates them using its future concentrations in the atmosphere by using a trained surrogate of a numerical transport model by maintaining consistency between the emissions, atmospheric transport, and future concentrations. The surrogate model is trained in a data-driven way to improve transport models, which is less complicated and time-consuming than correcting the parameterizations and constants used in numerically modeling various processes in transport.