Satellite Remote Sensing and Machine Learning for Advanced Atmospheric Monitoring and Forecasting

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 654

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Guest Editor
Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, 10679 Athens, Greece
Interests: satellite remote sensing; satellite meteorology; satellite climatology; GIS analysis; atmospheric environment
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Special Issue Information

Dear Colleagues,

Satellite remote sensing has emerged as a cornerstone of modern atmospheric science and weather and climate monitoring, driven by continuous advances in sensor technology and data processing capabilities. Numerous satellite missions such as Sentinel-5P and Meteosat provide high-resolution, multi-spectral observations that enable systematic monitoring of the Earth’s atmosphere in many different spatiotemporal scales. These observations support the analysis of atmospheric composition, cloud dynamics, precipitation systems, and extreme weather events, all of which are central to understanding the atmosphere, the weather variability and the long-term climate change. Towards this, natural and anthropogenic emissions of aerosols and trace gases significantly influence radiative forcing, cloud microphysics, and the hydrological cycle. Accurate detection and quantification of these constituents from space are therefore essential. However, the increasing volume, complexity, and heterogeneity of satellite datasets pose substantial analytical challenges.

Machine learning offers transformative opportunities for advanced atmospheric monitoring and forecasting. By enabling automated feature extraction, nonlinear pattern recognition, data fusion, and uncertainty quantification, machine learning techniques enhance retrieval algorithms, improve spatiotemporal analyses, and strengthen predictive models. The integration of satellite remote sensing with data-driven approaches thus represents a powerful framework for next-generation atmospheric research, operational forecasting, and climate resilience strategies.

Dr. Stavros Kolios
Guest Editor

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Keywords

  • satellite remote sensing
  • atmospheric monitoring
  • machine learning
  • aerosols
  • radiative forcing
  • cloud microphysics
  • trace gases
  • spatiotemporal analysis
  • extreme weather events
  • climate forecasting

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Published Papers (1 paper)

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Research

17 pages, 3032 KB  
Article
Impact of Optical Flow and Joint Loss on Nowcasting of Severe Convective Weather at Airports
by Qin Wang, Youfang Zhang and Lieshuang Liu
Atmosphere 2026, 17(5), 497; https://doi.org/10.3390/atmos17050497 - 14 May 2026
Viewed by 334
Abstract
With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 [...] Read more.
With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 satellite imagery, surface observations, and radar optical flow features to nowcast multiple severe convective events within the next 30 min. The model uses 2D-CNN for spatial extraction, ConvLSTM for temporal dynamics, and a weighted joint loss (Focal Loss and Dice Loss) to address class imbalance. Trained on 396 samples (positive-to-negative ratio 1:2.5) from 83 events at Guanghan Airport (2021–2024), incorporating optical flow features significantly boosted performance: macro-F1 increased from 0.719 to 0.792, and Threat Score (TS) from 0.567 to 0.705. Notably, false negatives for minority classes dropped sharply, with strong winds F1-score rising from 0.15 to 1.00. Ablation analysis showed optical flow as the top contributor (Mean Decrease in TS ≈ 0.5). Through multi-modal fusion and motion enhancement, this interpretable model provides high-precision nowcasting for airport severe convective weather, offering substantial value for aviation safety. Full article
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