Abstract. MIPAS, the Michelson Interferometer for Passive Atmospheric Sounding, is a mid-infrared emission spectrometer which is part of the core payload of ENVISAT. It is a limb sounder, i.e. it scans across the horizon detecting atmospheric spectral radiances which are inverted to vertical temperature, trace species and cloud distributions. These data can be used for scientific investigations in various research fields including dynamics and chemistry in the altitude region between upper troposphere and lower thermosphere. The instrument is a well calibrated and characterized Fourier transform spectrometer which is able to detect many trace constituents simultaneously. The different concepts of retrieval methods are described including multi-target and two-dimensional retrievals. Operationally generated data sets consist of temperature, H2O, O3, CH4, N2O, HNO3, and NO2 profiles. Measurement errors are investigated in detail and random and systematic errors are specified. The results are validated by independent instrumentation which has been operated at ground stations or aboard balloon gondolas and aircraft. Intercomparisons of MIPAS measurements with other satellite data have been carried out, too. As a result, it has been proven that the MIPAS data are of good quality. MIPAS can be operated in different measurement modes in order to optimize the scientific output. Due to the wealth of information in the MIPAS spectra, many scientific results have already been published. They include intercomparisons of temperature distributions with ECMWF data, the derivation of the whole NOy family, the study of atmospheric processes during the Antarctic vortex split in September 2002, the determination of properties of Polar Stratospheric Clouds, the downward transport of NOx in the middle atmosphere, the stratosphere-troposphere exchange, the influence of solar variability on the middle atmosphere, and the observation of Non-LTE effects in the mesosphere.

This is a preprint of an article accepted to be published in a special issue of Scientometrics: Gläser, J., Scharnhorst, A. and Glänzel, W. (eds), Same data -different results? Towards a comparative approach to the identification of thematic structures in science. This is the last paper in the Synthesis section of the special issue on 'Same Data, Different Results'. We first provide a framework of how to describe and distinguish approaches to topic extraction from bibliographic data of scientific publications. We then compare solutions delivered by the different topic extraction approaches in this special issue, and explore where they agree and differ. This is achieved without reference to a ground truth, since we have to assume the existence of multiple, equally important, valid perspectives and want to avoid bias through the adoption of an ad-hoc yardstick. Instead, we apply different ways to quantitatively and visually compare solutions to explore their commonalities and differences and develop hypotheses about the origin of these differences. We conclude with a discussion of future work needed to develop methods for comparison and validation of topic extraction results, and express our concern about the lack of access to non-proprietary benchmark data sets to support method development in the field of scientometrics.

Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build representations of these documents which keep their semantics as much as possible and are also suitable for efficient similarity calculation. The metadata of articles in the Astro dataset contribute to a semantic matrix, which uses a vector space to capture the semantics of entities derived from these articles and consequently supports the contextual exploration of these entities in LittleAriadne. However, this semantic matrix does not allow to calculate similarities between articles directly. In this paper, we will describe in detail how we build a semantic representation for an article from the entities that are associated with it. Base on such semantic representations of articles, we apply two standard clustering methods, K-Means and the Louvain community detection algorithm, which leads to our two clustering solutions labelled as OCLC-31 (standing for K-Means) and OCLC-Louvain (standing for Louvain). In this paper, we will give the implementation details and a basic comparison with other clustering solutions that are reported in this special issue.Comment: Special Issue of Scientometrics: Same data - different results? Towards a comparative approach to the identification of thematic structures in scienc

This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for an innovative information exploration tool, called Ariadne, which operates on bibliographic databases with tens of millions of records [18]. In this paper, the method behind Ariadne is further developed and applied to the research question of the special issue "Same data, different results" -the better understanding of topic (re-)construction by different bibliometric approaches. For the case of the Astro dataset of 111,616 articles in astronomy and astrophysics, a new instantiation of the interactive exploring tool, LittleAriadne, has been created. This paper contributes to the overall challenge to delineate and define topics in two different ways. First, we produce two clustering solutions based on vector representations of articles in a lexical space. These vectors are built on semantic indexing of entities associated with those articles. Second, we discuss how LittleAriadne can be used to browse through the network of topical terms, authors, journals, citations and various cluster solutions of the Astro dataset. More specifically, we treat the assignment of an article to the different clustering solutions as an additional element of its bibliographic record. Keeping the principle of semantic indexing on the level of such an extended list of entities of the bibliographic record, LittleAriadne in turn provides a visualization of the context of a specific clustering solution. It also conveys the similarity of article clusters produced by different al-

Abstract. The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA) in cooperation with the Japan Aerospace Exploration Agency (JAXA) to measure global profiles of aerosols, clouds and precipitation properties together with radiative fluxes and derived heating rates. The data will be used in particular to evaluate the representation of clouds, aerosols, precipitation and associated radiative fluxes in weather forecasting and climate models. The satellite scientific payload consists of four instruments, a lidar, a radar, an imager and a broad-band radiometer. The measurements of these instruments are processed in the ground segment, which produces and distributes the science data products. The EarthCARE observational requirements are addressed. An overview is given of the space segment with a detailed description of the four science instruments. Furthermore, the elements of the Space Segment and Ground Segment that are relevant for the science data users are described.