The Cluster Science Archive and its relevance for multi-missions data analysis

Abstract:

The science data archive of the Cluster mission is a major contribution of the European Space Agency (ESA) to the International Living With a Star program. Known as the Cluster Active Archive (CAA), its availability since 2006 has resulted in a significant increase of the scientific return of this on-going mission.

The Cluster science archive (CSA) has been developed in parallel to CAA over the last few years at the European Space Astronomy Center (ESAC) in Madrid, Spain. It is the long-term science archive of the Cluster mission, developed and managed along with all the other ESA science archives. Publicly opened in November 2013, CSA is available in parallel with CAA during a transition period until CAA public closing in early autumn 2014.

Our goal here is to present what has been put in place to help geophysicists in their research. We will first talk about some aspects of the CSA user interface (data visualization including particle distribution; user data profiles) and how users can access data remotely (data streaming in Matlab, or via IDL or Python).

The second goal is to present unique value added datasets that are now available on the CSA/CAA. These data have been produced by the scientific community, thanks to two EU FP7 projects: ECLAT and MAARBLE.

For instance, the polarization and propagation parameters of ULF Pc waves measured by Cluster and Themis (since 2007) are available and cover more than a decade; along with magnetic spectra of Pc waves measured simultaneously by CHAMP and ground-based magnetometers. These data are clearly an outstanding data resource for low frequency waves researchers.

Other datasets will be presented to show that CSA/CAA allow much more than downloading Cluster data from a graphical user interface. It's a single point entry that allows studies from micro-scale physics in the tail (e.g. catalogues of dipolarization fronts), to meso- and large-scale M-I coupling studies (e.g. Cluster magnetic footprints based on T96 and TS05; selected SuperDARN maps, MIRACLE network data and NASA IMAGE FUV auroral images). Plasma regions crossed on the nightside and very soon on the dayside are also there to ease the user in its data mining. Upcoming data and services will also be evoked.