A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were performed using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by RV Polarstern. The dataset contains retrieved optical depths and effective radii of ice and liquid water, from which the liquid water path and ice water path are calculated. The water paths and the effective radii retrieved from the FTIR measurements are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. The purpose of this comparison is to benchmark the infrared retrieval data against the established Cloudnet retrieval. For the liquid water path, the data correlate, showing a mean bias of 2.48 g m⁻² and a root-mean-square error of 10.43 g m⁻². It follows that the infrared retrieval is able to determine the liquid water path. Although liquid water path retrievals from the Cloudnet retrieval data come with an uncertainty of at least 20 g m⁻², a root-mean-square error of 9.48 g m⁻² for clouds with a liquid water path of at most 20 g m⁻² is found. This indicates that the liquid water paths, especially of thin clouds, of the Cloudnet retrieval can be determined with higher accuracy than expected. Apart from this, the dataset of microphysical cloud properties presented here allows researchers to perform calculations of the cloud radiative effects when the Cloudnet data from the campaign are not available, which was the case from 22 July 2017 until 19 August 2017. The dataset is published at PANGAEA (https://doi.org/10.1594/PANGAEA.933829, Richter et al., 2021).

中文翻译：

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从 2017 年北极夏季测量的傅里叶变换红外 (FTIR) 发射光谱中检索到的微物理云参数数据集

介绍了从 2017 年夏季在北极测量的红外光谱辐射中检索到的光学薄云的微物理云参数数据集。使用 RV Polarstern携带的移动傅里叶变换红外 (FTIR) 光谱仪进行测量 . 该数据集包含检索到的冰和液态水的光学深度和有效半径，据此计算液态水路径和冰水路径。从 FTIR 测量中检索到的水路和有效半径与从云雷达、激光雷达和微波辐射计联合测量协同检索（称为 Cloudnet）中导出的量进行比较。此比较的目的是将红外检索数据与已建立的 Cloudnet 检索进行基准比较。对于液态水路径，数据相互关联，平均偏差为 2.48  g m ^-2，均方根误差为 10.43  g m ^-2. 由此可见，红外反演能够确定液态水的路径。尽管来自 Cloudnet 反演数据的液态水路径反演具有至少 20  g m ^-2的不确定性，但对于液态水路径最多为 20  g m ^-2的云，其均方根误差为 9.48  g m ^-2被发现。这表明 Cloudnet 反演的液态水路径，尤其是薄云的液态水路径可以比预期的精度更高。除此之外，此处提供的微物理云特性数据集允许研究人员在无法获得该活动的 Cloudnet 数据时（2017 年 7 月 22 日至 2017 年 8 月 19 日的情况）执行云辐射效应计算。数据集已发布在 PANGEA （https://doi.org/10.1594/PANGAEA.933829，Richter 等人，2021 年）。