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

Abstract

Gas concentration estimation is crucial for understanding and mitigating climate change. While most research and monitoring efforts focus on major greenhouse gases such as CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, significantly less attention has been given to trace gases like NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, 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 NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> serves as the target trace gas in this study, the proposed framework is potentially applicable to the prediction of other atmospheric gas concentrations.