Cloud Classification by Machine Learning for Geostationary Radiation Imager

Guo, Bin; Zhang, Feng; Li, Wenwen; Zhao, Zhijun

doi:10.1109/tgrs.2024.3353373

Cited by 8 publications

(2 citation statements)

References 51 publications

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“…Qualitatively, we inspected the model's ability in categorizing different cloud types, boundaries, and detail structures by comparing classification recognition differences between the validation set and test set. Quantitatively, metrics including precision, recall and F1-score were used to assess the model (Dev et al, 2017;Guo et al, 2024). Precision reflects the portion of true positive cases among samples predicted as positive, and is calculated as:…”

Section: Cloud Classification Evaluation Indicatorsmentioning

confidence: 99%

Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging

Luo,

Pan,

et al. 2024

Preprint

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Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

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Section: Cloud Classification Evaluation Indicatorsmentioning

confidence: 99%

Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging

Luo,

Pan,

et al. 2024

Preprint

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“…Cloud segmentation based on deep learning is a long-standing and ongoing area of research in the field of remote sensing. This includes general cloud segmentation without differentiating cloud types [5,11,14,[17][18][19][20][21][22], as well as segmentation of severe convective clouds [2,4,6,8,15,[23][24][25][26][27], which continue to be actively studied, underscoring the enduring significance of this problem. In practical applications, particularly where real-time processing of vast datasets is required, lightweight neural networks have garnered attention due to their lower computational demands and rapid processing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling

Zhang,

2024

Remote Sensing

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This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective cloud dataset by a combination of algorithms and expert judgment. Through the ablation study of a model ensembling combination of multiple specialized lightweight architectures—ENet, ESPNet, Fast-SCNN, ICNet, and MobileNetV2—the optimal EFNet (ENet- and Fast-SCNN-based network) not only achieves real-time processing capabilities but also ensures high accuracy in severe weather detection. EFNet consistently outperformed traditional, heavier models across several key performance indicators: achieving an accuracy of 0.9941, precision of 0.9391, recall of 0.9201, F1 score of 0.9295, and computing time of 18.65 s over the test dataset of 300 images (~0.06 s per 512 × 512 pic). ENet shows high precision but misses subtle clouds, while Fast-SCNN has high sensitivity but lower precision, leading to misclassifications. EFNet’s ensemble approach balances these traits, enhancing overall predictive accuracy. The ensemble method of lightweight models effectively aggregates the diverse strengths of the individual models, optimizing both speed and predictive performance.

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Retrieval of Cloud Macro‐Physical Properties Using theFY‐4A Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS)

Guo,

Zhang,

Zhao

et al. 2024

Geophysical Research Letters

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This study presents a novel approach for conducting all‐day retrieval of cloud macro‐physical properties (single‐layer cloud phase, cloud top height, and cloud base height for optical thickness less than 10) using the Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the geostationary meteorological satellite Fengyun‐4A based on machine learning methods. Model accuracy was compared after integrating ECMWF Reanalysis v5 (ERA‐5) data, atmospheric temperature and moisture profiles, and GIIRS clear‐column radiance. Results demonstrate that integrating GIIRS clear‐column radiances can enhance the precision of cloud phase classification and the retrieval of cloud macro‐physical properties. This effectively replaces the role of atmospheric temperature and humidity profiles, which are typically required for thermal infrared remote sensing retrieval. Moreover, the issue of delayed acquisition of ERA‐5 atmospheric temperature and humidity profiles is mitigated, enabling near real‐time and all‐day retrieval of cloud macro‐physical properties.

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Cloud Classification by Machine Learning for Geostationary Radiation Imager

Cited by 8 publications

References 51 publications

Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging

Innovative Cloud Quantification: Deep Learning Classification and Finite Sector Clustering for Ground-Based All Sky Imaging

Methodology for Severe Convective Cloud Identification Using Lightweight Neural Network Model Ensembling

Retrieval of Cloud Macro‐Physical Properties Using theFY‐4A Advanced Geostationary Radiation Imager (AGRI) and the Geostationary Interferometric Infrared Sounder (GIIRS)

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