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Introducing a New Partial Convolutional Neural Network for IASI Cloud Classification
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DOI：10.1029/2025JD045546.png)
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Abstract
En 中文
Clouds cover approximately 60% of the globe and are therefore an obstacle to observing the atmosphere and surface of the Earth from space. To limit their impact on Infrared Atmospheric Sounding Interferometer (IASI)-based atmospheric and surface property retrievals, it is important to obtain an IASI-coherent cloud detection/classification. Many cloud retrievals, whether physical or statistical, are performed at the pixel-level. However, since clouds are spatially structured, using the spatial coherency across the IASI footprints should improve cloud detection. Infrared Atmospheric Sounding Interferometer orbits (restructured as rectangular images) are collocated with the cloud classification (clear, water, ice, and two-level ice) extracted from SEVIRI-based Optimal Cloud Analysis to train a machine learning model. The training is performed over the SEVIRI disk, but the resulting model can be applied at the global scale (i.e., transfer learning). We use a partial-Convolutional Neural Networks (p-CNN), a new image-scale model able to deal with a large amount of spatially missing pixels. This p-CNN model correctly classifies the four cloud types with accuracy 77%; and this number increases to 88% when considering only spatially homogeneous IASI pixels, which shows the importance of subpixel heterogeneity. The other main source of differences is the IASI/SEVIRI resolution discrepancy. Thanks to our image-processing approach and the cloud spatial coherency, two-layer clouds are better retrieved than with pixel-wise processing. Our new IASI cloud product not only classifies the cloud phase at a global scale, but also estimates the cloud-type fractions in each IASI pixel. It therefore has a potential for subpixel downscaling.
Keywords:
IASI
machine learning
convolutional neural networks
cloud classification
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J
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3.4
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288
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