用卫星图像预测云中冰水含量和冰晶浓度的三维分布。
IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI
- 基于ConvNeXt U-Net与3D PatchGAN，从静止卫星数据重建垂直云结构。
- 生成10年高分辨率三维冰含量数据集，覆盖范围超DARDAR数万倍。
- 适用于气候研究与气象预报，尤其适合缺乏主动雷达的区域。
IceCloudNet是一种基于机器学习的新方法，可预测高质量、垂直分辨的云冰水含量（IWC）和冰晶数浓度（N$_\textrm{ice}$）。其预测结果兼具地球同步卫星观测（SEVIRI）的时空覆盖与分辨率，以及主动卫星反演（DARDAR）的垂直分辨率。模型由基于ConvNeXt的U-Net和3D PatchGAN判别器组成，通过共位的SEVIRI图像预测DARDAR廓线进行训练。尽管因过境范围窄导致DARDAR数据稀疏，该模型仍能高精度还原云出现概率、空间结构及微物理特性。模型已应用于十年的SEVIRI数据，生成了覆盖30°W至30°E、30°S至30°N区域，分辨率为3 km×3 km×240 m×15分钟的冰云三维数据集。该数据集在DARDAR可用时段内使垂直云廓线可用性提升超过六数量级，并且能生成超出原有卫星任务寿命的垂直云廓线。
原文摘要 · Abstract (English)
IceCloudNet is a novel method based on machine learning able to predict high-quality vertically resolved cloud ice water contents (IWC) and ice crystal number concentrations (N$_\textrm{ice}$). The predictions come at the spatio-temporal coverage and resolution of geostationary satellite observations (SEVIRI) and the vertical resolution of active satellite retrievals (DARDAR). IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, spatial structure, and microphysical properties with high precision. The model has been applied to ten years of SEVIRI data, producing a dataset of vertically resolved IWC and N$_\textrm{ice}$ of clouds containing ice with a 3 kmx3 kmx240 mx15 minute resolution in a spatial domain of 30°W to 30°E and 30°S to 30°N. The produced dataset increases the availability of vertical cloud profiles, for the period when DARDAR is available, by more than six orders of magnitude and moreover, IceCloudNet is able to produce vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR.
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