Remote Sensing and Atmospheric Modelling: Data, Processes, Integration and Future Directions - Suyog Gupta (International Journal of Atmosphere, 2026)
Atmospheric modelling plays a central role in weather forecasting, climate projection, and air quality assessment; however, the availability, accuracy, and representativeness of atmospheric observations fundamentally constrain its reliability. Over the past two decades, rapid advances in remote sensing (RS) have transformed atmospheric observation by providing spatially continuous, multiscale measurements of key atmospheric variables, including aerosols, trace gases, clouds, precipitation, and atmospheric thermodynamic profiles. This review synthesises recent progress in integrating RS observations with atmospheric modelling frameworks, with a specific focus on observation–model synergy rather than sensors or models in isolation. We examine the physical foundations of atmospheric models, major RS platforms and retrieval principles, and the role of data assimilation in coupling observations with the model dynamics. Beyond assimilation, this review highlights approaches for model initialisation, forcing, evaluation, bias correction, and downscaling using satellite-derived products. Key applications in weather prediction, air quality modelling, climate monitoring, and regional-to-urban studies are discussed, illustrating the practical value of integrated observation–model systems in these fields. Emerging machine learning (ML) and hybrid physical–data-driven approaches were assessed for their potential to enhance computational efficiency and represent unresolved processes, alongside their limitations in interpretability and uncertainty quantification. Finally, this review identifies persistent challenges, including retrieval uncertainty, scale mismatch, model structural bias, and data continuity. It outlines future research directions involving next-generation observing systems, multi-sensor data fusion, coupled Earth system modelling, and atmospheric digital twins. Together, these developments point toward more adaptive, observation-driven atmospheric modelling frameworks capable of supporting robust scientific understanding and decision-making in changing climates
Keywords: Air quality; Climate; Data assimilation; Machine Learning; atmospheric models; remote sensing