M-VRE: The MOSAiC - Virtual Research Environment
The M-VRE project is funded by the German BMBF initiative „MARE:N – Polarforschung/MOSAiC“ from July 2021 to June 2024. This project is a collaboration of AWI Bremerhaven, DKRZ Hamburg and DLR Jena.
The main goal of the M-VRE project is to provide novel methods and software tools for the MOSAiC consortium, the global climate community, and the general public for efficiently exploring, analysing, and visualizing MOSAiC data in an online and user-friendly manner. This enables scientists to exploit large, complex, and heterogeneous data sets to answer fundamental research questions of the MOSAiC expedition, connected to the overarching goal of the identification of reasons and consequences of changing and shrinking sea ice cover. To achieve this, a web-based data analysis and exploitation environment, a so-called "virtual research environment (VRE)", will be developed as part of the AWI cloud infrastructure. The collaborative and reproducible online analysis of MOSAiC data opens up new possibilities in climate research and increases the visibility and usability of the MOSAiC mission. The VRE offers online data analysis (e.g. quality control, interpolation), exploration (data cubes), and visualization (maps, scatter plots, time series, section plots and more) of MOSAiC data as well as automated data quality control through Deep Learning methods.
Watch the M-VRE overview video below!

webODV, the online pendant of the desktop Ocean Data View (ODV, https://odv.awi.de) software. ODV, with its diverse functionality, is widely used in the marine and environmental sciences for a large variety of applications. Currently we have approx. 10,000 users worldwide. Its features include creating maps, surface plots, section plots, scatter plots, filtering data, extracting data subsets from large collections, aggregating single data files into larger datasets and collections, flagging data, and modifying data, among others. webODV is a highly interactive tool running in the browser, where the user can interact directly with the data in the maps or plots. Productive webODV services for the GEOTRACES and EMODnet Chemistry projects are operated at https://webodv.awi.de.
Data Cubes. Raster data management systems, such as Rasdaman or SciDB, and data cube frameworks, such as openDataCube and EarthSystemDataCube, provide efficient data access, query processing, and data analysis support for large-scale scientific multi-dimensional data. As such, it serves as a foundation and backend for complex scientific data analyses and offers intuitive programming interfaces (e.g., in scientific scripting languages, such as Python or R) for scientific users, hence perfectly suited in the M-VRE. We will develop a data cube service that allows querying raster data from the MOSAiC expedition and connect it as a backend service to other data analysis and visualization services offered in the M-VRE.
DIVAnd. DIVAnd, the Data-Interpolating Variational Analysis in n dimensions, is a powerful interpolation tool developed by the GeoHydrodynamics and Environment Research group at the University of Liège. It allows to create smooth and continuous fields in one, two, three or n dimensions from a set of observations, while handling constraints which are typical for earth-science applications (e.g. non-regularly located data points, physical constraints). This is a recurring task in a wide range of scientific disciplines like physical oceanography We are already working closely together with the developers of DIVAnd (Dr. A. Barth and Dr. C. Troupin) and will invite them to our regularly planned workshops for teaching MOSAiC users. More infos at https://github.com/gher-ulg/divand.jl.
Photo by Florian Weihmann from Pexels
Image by Florian Weihmann from Pexels.
AutoQC. Until today the manual and visual quality control (QC) of ocean data is a time consuming effort done by ocean experts. With increasing amounts of big data, algorithmic help is urgently needed, where artificial intelligence could play a dominant role. We develop deep learning algorithms for automated QC of ocean data to support scientists. We will train the algorithms with AWIs UDASH data to "learn" arctic QC. We will then apply the algorithms to new MOSAiC data together with the ocean experts to create a novel quality controlled arctic hydrographic data set. More infos about our previous project at https://salacia-ml.awi.de.