ARCHIVAL OF THE CLUSTER ION SPECTROMETRY (CIS) DATAIN THE CLUSTER ACTIVE ARCHIVE (CAA)

摘要

The Cluster Active Archive (CAA) aims at preservingrnthe 4 Cluster spacecraft data, so that they are usable inrnthe long-term by the scientific community as well as byrnthe instrument team Pis and Co-Is. This implies that therndata are filed together with the descriptive andrndocumentary elements making it possible to select andrninterpret them. The CIS (Cluster Ion Spectrometry)rnexperiment is a comprehensive ionic plasma spectrometryrnpackage onboard the four Cluster spacecraft,rncapable of obtaining full three-dimensional ionrndistributions (about 0 to 40 keV/q) with a timernresolution of one spacecraft spin (4 sec) and with massper-rncharge composition determination. The CIS packagernconsists of two different instruments, a Hot IonrnAnalyser (HIA) and a time-of-flight ion CompositionrnDistribution Function (CODIF) analyser, plus arnsophisticated dual-processor based instrument controlrnand data processing system (DPS). For the archival ofrnthe CIS data a multi-level approach has been adopted.rnThe CAA archival will include processed raw datarn(Level 1 data), moments of the distribution functionsrn(Level 2 data), and calibrated high-resolution data in arnvariety of physical units (Level 3 data). Furthermore,rnthe calibration files and high-level processing softwarernwill be also archived. This approach provides “ready tornuse” high-resolution data products, archived in ASCIIrn(CEF-2 format), which are expected to have a longrnlifetime. However, should a user wish to re-analyse thernraw data and the instrument calibrations, he will also bernable to do it. The CIS data archive will also includernexperiment documentation, graphical products forrnbrowsing through the data, and data caveats. Given therncomplexity of an ion spectrometer, and the variety of itsrnoperational modes, each one being optimised for arndifferent magnetospheric region or measurementrnobjective, consultation of the data caveats by the endrnuser will always be a necessary step in the data analysis.