Bridging In-Situ and Satellite Data: Enhancing Gas Concentration Estimation Through Integration of Data-Driven and Physics-Based Modeling

Le résumé fourni par la source

Gas concentration estimation is crucial for understanding and mitigating climate change. While most research and monitoring efforts focus on major greenhouse gases such as CO2, significantly less attention has been given to trace gases like NO2, which play a critical role in atmospheric chemistry and air quality. This paper aims to enhance trace gas concentration estimation by integrating physics-based models into data-driven neural network frameworks. Furthermore, to improve large-scale estimation accuracy, we incorporate in-situ measurements to refine neural network models trained on satellite observations. The resulting model can provide reliable large-scale gas concentration estimates, particularly for locations lacking precise in-situ measurements. This approach offers a novel pathway to enhance the accuracy and applicability of gas monitoring for climate and environmental research. While NO2serves as the target trace gas in this study, the proposed framework is potentially applicable to the prediction of other atmospheric gas concentrations.

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