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In recent years, intense usage of computing has been the main strategy of investigations in several scientific research projects. The progress in computing technology has opened unprecedented opportunities for systematic collection of experimental data and the associated analysis that were considered impossible only few years ago.

This paper focuses on the strategies in use: it reviews the various components that are necessary for an effective solution that ensures the storage, the long term preservation, and the worldwide distribution of large quantities of data that are necessary in a large scientific research project.

The paper also mentions several examples of data management solutions used in High Energy Physics for the CERN Large Hadron Collider (LHC) experiments in Geneva, Switzerland which generate more than 30,000 terabytes of data every year that need to be preserved, analyzed, and made available to a community of several tenth of thousands scientists worldwide.

Cloud-resolving atmospheric general circulation models using large-scale supercomputers reproduce realistic behavior of 3-dimensional atmospheric field on a global scale. To understand the simulation result for scientists, conventional visualization methods based on 2-dimensional cloud classification are not enough for understanding individual clouds and their physical characteristics. In this study, we propose a new 3-dimensional extraction and classification method of simulated clouds based on their 3-dimensional shape and physical properties. Our proposed method extracts individual clouds by cloud water and cloud ice, and classifies them into six types by their altitude and upward flow. We applied the method to time-varying atmospheric simulation data, and attempted to visualize atmospheric phenomena on the tropics such as developing cumulonimbus and tropical cyclone. Two case studies clearly visualize the behavior of individual cloud type and clarify that some cloud types have a relationship with rainfall during active weather phenomena. The proposed method has the potential to analyze such phenomena that develop in the vertical direction as well as the horizontal direction.