IceCloudNet：基于稀疏监督学习的SEVIRI输入卷云与混合相云预测
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
AI总结：
针对含冰云在气候预测中不确定性大的问题，研究提出IceCloudNet，用三年SEVIRI和DARDAR数据训练卷积神经网络，构建兼具高时空覆盖与反演质量的冰微物理特性观测约束，助力冰云研究与气候不确定性降低。
摘要：
含冰粒子的云在气候系统中发挥着关键作用，但它们仍是气候模型和未来气候预测中极大不确定性的来源。本研究构建了一种新的、依赖云型的冰微物理特性观测约束，兼具地球静止卫星仪器的时空覆盖范围与主动卫星反演的质量。我们通过在三年的SEVIRI和DARDAR数据集上训练卷积神经网络实现了这一目标。本工作将助力开展改进冰云过程理解的新研究，进而减少气候变化中的不确定性，并帮助评估卷云的地球工程方法。
英文摘要：
Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
