A Content-Based Database System for Large Volume Climate Data
Abstract
Analyses of large ensemble data of future climate are quite useful in order to provide probabilistic future projection of climate change in various interdisciplinary fields related to, e.g., hydrology, civil engineering and adaptation planning in response to global warming. Ensemble data of "+2K near-future climate simulations" are currently generated by "Social Implementation Program on Climate Change Adaptation Technology" (SI-CAT) as a part of a database for Policy Decision making for Future climate change (d4PDF; Mizuta et al. 2016) produced by Program for Risk Information on Climate Change. Those data consist of global warming simulations and regionally downscaled simulations around Japan. Considering that data volumes can be on the order of a few petabytes, there is a demand for a more user-friendly search and download method that caters to specific requests of the user.
In order to provide functions to extract necessary data from +2K near-future climate simulations to the users, we developed "System for Efficient content-based retrieval to Analyze Large volume climate data (SEAL)". Existing web-based search systems for climate simulations are designed to find data files using metadata associated with a data file itself. On the other hand, SEAL allows the users to extract necessary data using metadata associated with contents of a data file such as physical values. SEAL mainly consists of a relational database, a data download function and web-based user interface. Among the three main features, a key role is provided by the relational database using PostgreSQL which is designed to be registered temporally and spatially compressed data. The data download function allows users to extract spatio-temporal data based on search results obtained by the relational database. In addition, the web-based user interface allows the users to easily use the relational database without knowledge about PostgreSQL. SEAL is currently in operational test on a server maintained by Data Integration and Analysis System Program (DIAS). SEAL will be released on DIAS in Japanese fiscal year 2019. Techniques of SEAL might be quite useful for simulation, experimental and observational data in other research fields. We report functions, advantages and some case studies of SEAL.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN51A..06N
- Keywords:
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- 0399 General or miscellaneous;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3399 General or miscellaneous;
- ATMOSPHERIC PROCESSES;
- 1899 General or miscellaneous;
- HYDROLOGY;
- 1996 Web Services;
- INFORMATICS