A dataset of microphysical cloud parameters, retrieved from Fourier-transform infrared (FTIR) emission spectra measured in Arctic summer 2017

• Published in: Earth System Science Data
• Main Authors: Richter, Philipp, Palm, Mathias, Weinzierl, Christine, Griesche, Hannes, Rowe, Penny M., Notholt, Justus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Arctic
• Online Access: https://doi.org/10.5194/essd-14-2767-2022; https://noa.gwlb.de/receive/cop_mods_00061568; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061012/essd-14-2767-2022.pdf; https://essd.copernicus.org/articles/14/2767/2022/essd-14-2767-2022.pdf

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).