Welcome to the world of AC3 airborne data! It is a collection of python code examples to get you started with the airborne data collected during the various airborne campaigns within the German DFG project - TRR 172, “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC3) Wendisch et al. [WBB+17], a joint research initiative of the Universities Leipzig (Leipzig Institute of Meteorology, LIM), Cologne (Institute of Geophysics and Meteorology, IGM), and Bremen and of the research institutes TROPOS (Leipzig) and Alfred Wegener Institute (AWI Bremerhaven and Potsdam). The document is permantently growing and changing and might never be finished. It follows the concept and ideas of the work done within the EUREC4A community.


The datasets presented here are accessible online, i.e., you don’t have to download anything in advance. Most datasets are accessed via the ac3airborne intake catalog, which simply said takes care of the links to datasets in their most recent version. Many of the datasets are publically available and are located on the PANGAEA database and have a digital object identifier (DOI). Data not yet published is on the Nextcloud server of the AC3 project. These data are still preliminary or never planed to be published on PANGAEA. These datasets, that are mostly from the recent HALO-(AC)3sup> campaign, are still under moratorium and a login is required. Some of the data that is publically available and only post-processed to fit the needs, are hosted on a server at University of Cologne.

By implemented caching capabilities, it is only necessary to download the data the first time they are used. The scripts typically contain at minimum a description on how to get a specific dataset and some simple plots of basic quantities. Most chapters include additional information from aircraft flight segments or further meta data, sometimes more sophisticated plots, or also a combination of variables from different datasets. In addition, some small tools are included to work with auxilliary data like sea ice coverage or land-mask.