From Satellites to Sensors: Harnessing AI to Unify Multi-Scale Data in Modern Atmospheric Monitoring

Authors

  • Yan Wu

    School of Electronics and Information Engineering, Liaoning Institute of Technology, Benxi 117004, China

DOI:

https://doi.org/10.30564/jees.v8i2.12929
Received: 12 December 2025 | Revised: 1 February 2026 | Accepted: 4 February 2026 | Published Online: 5 February 2026

Abstract

Software-defined, data-intensive cyber-physical systems and software-defined networks of atmospheric observers are evolving rapidly due to the rapid expansion of sensing diversity, the volume of streaming data, and the demand for low-latency, decision-relevant products. Simultaneously, artificial intelligence (AI) and the continuously evolving state of computing are making it possible to create end-to-end architecture fostering the migrations of the presumably single algorithm to combined intelligent ingestion, quality control, and multi-modal fusion, uncertainty-related retrieval, and scalable service delivery at the edge-to-cloud-high-performance computing (HPC) environment. This overview summarizes AI-based models of future atmospheric observation networks within a single, consolidated taxonomy based on deployment topology, learning and update modes, connectivity to physical models and data assimilation, level of autonomy (passive to adaptive sensing), and model of governance. Next, we consider recurring architectural themes, such as edge intelligence and streaming provenance and machine learning operations (MLOps)/model operations (ModelOps) to continue evaluation and safely update, and we scrutinize integration gateways with physical models, like data-assimilation-oriented outputs, hybrid/physics-informed designs, and simulation of observing systems using digital twins. Lastly, we address evaluation and readiness aspects that are not limited to predictive skill, but also involve calibrated uncertainty, nonstationary and extreme robustness, system latency and reliability, interoperability, security, and demonstrated downstream influence on analyses and forecasts. Through bringing together the cross-cutting issues and prospects, this review provides a road map with respect to trustworthy, interoperable, and sustainable observation infrastructures in which code and climate science will co-evolve.

Keywords:

Atmospheric Observation Networks; Data Assimilation; Edge AI; Uncertainty Quantification; Digital Twins

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How to Cite

Wu, Y. (2026). From Satellites to Sensors: Harnessing AI to Unify Multi-Scale Data in Modern Atmospheric Monitoring. Journal of Environmental & Earth Sciences, 8(2), 72–104. https://doi.org/10.30564/jees.v8i2.12929

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Vol. 8 , Iss. 2 (February 2026)

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Review

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Copyright © 2026 Yan Wu

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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