Multi-Source Atmospheric Remote Sensing: Enabling High-Precision Meteorological Monitoring and Forecasting

Dear Colleagues,

The integration of multi-source atmospheric remote sensing has revolutionized meteorological monitoring and forecasting by providing high-resolution, multi-dimensional observations of the atmosphere. With advancements in satellite, ground-based, and airborne remote sensing technologies, as well as the development of sophisticated data assimilation techniques, it is now possible to achieve a more precise and dynamic characterization of atmospheric processes. These innovations are critical for improving the accuracy of weather prediction and mitigating the impacts of extreme meteorological events. 

However, challenges remain in effectively integrating heterogeneous remote sensing data, addressing observational gaps, and enhancing the synergy between observations and numerical models. The rapid evolution of artificial intelligence and big data analytics further presents new opportunities to unlock the potential of multi-source atmospheric remote sensing. By leveraging these cutting-edge technologies, researchers can advance the field of fine-scale meteorological monitoring and forecasting, ultimately supporting disaster prevention and sustainable development. 

For this Special Issue, we welcome contributions from researchers in atmospheric sciences, remote sensing, data assimilation, and environmental modeling to share their latest findings on the application of multi-source atmospheric remote sensing for refined weather monitoring and forecasting. 

In particular, we encourage studies investigating the following: 

• Multi-source remote sensing data fusion and assimilation
  Novel methodologies for integrating satellite, ground-based, UAV-based, and new-generation sensor observations (e.g., miniaturized, hyperspectral, or edge-computing-enabled devices) to improve atmospheric parameter retrievals and numerical weather prediction. 

• High-resolution atmospheric vertical sounding for fine-scale applications
  Advances in lidar, radar, hyperspectral, and microwave remote sensing for monitoring temperature, humidity, wind, and aerosol distributions, with an emphasis on applications requiring ultra-high spatiotemporal resolution (e.g., low-altitude economy, aviation safety, and urban microclimate). 

• AI-driven atmospheric remote sensing
  Applications of machine learning and deep learning in processing, analyzing, and interpreting multi-source remote sensing data for meteorological applications, including bias correction, feature extraction, and predictive modeling. 

• Extreme weather monitoring
  The utilization of remote sensing to study severe convective systems, typhoons, sandstorms, and other high-impact weather phenomena, with a focus on early warning systems. 

• Urban, regional, and low-altitude economy meteorological applications
  Remote sensing approaches for monitoring urban heat islands, air quality, boundary layer dynamics, and low-altitude meteorological conditions (e.g., drone operation corridors, vertiport safety, and wind shear detection) to support city-scale weather services and emerging low-altitude economic activities. 

We look forward to receiving your research contributions and reviews that will advance the understanding and application of multi-source atmospheric remote sensing in meteorology. 

Prof. Dr. Xiefei Zhi
Prof. Dr. Donglian Sun
Prof. Dr. Yonghong Zhang
Dr. Yan-An Liu
Dr. Wen Huo
Guest Editors

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• multi-source atmospheric remote sensing
• meteorological monitoring
• high-resolution vertical sounding
• data fusion and assimilation
• AI-driven analytics
• extreme weather prediction
• low-altitude economy meteorology
• air quality monitoring
• numerical weather prediction