Abstract
Analyses of large ensemble data on future climate are significantly useful for the probabilistic future projection of climate change in various interdisciplinary fields. However, the data volume of the Database for Policy Decision making for Future climate change or d4PDF, which is a mega-ensemble dataset, exceeds ∼ 3 PB, which is too large to download to local computers. To allow users for retrieve and downloading necessary data, we developed a user-friendly system called “System for Efficient content-based retrieval to Analyze Large volume climate data” (SEAL) under the Social Implementation Program on Climate Change Adaptation Technology (SI-CAT). Conventional web-based retrieval systems allow retrievals using metadata associated with a data file itself. In contrast, SEAL allows the users to retrieve the necessary data by using metadata associated with contents, such as physical values, of a data file. We confirmed that SEAL can reduce data sizes and total time required for obtaining necessary data to less than 0.5% and 1%, respectively, compared to conventional web-based retrieval systems.
Highlights
In the field of climate research, improvements in computer performances have led to significant growths in the volumes of large ensemble simulation data
The data sizes stored in the relational database of SEAL are reduced to ∼ 0.01% at a maximum and ∼ 0.3% at a minimum compared with the original data due to the use of the spatial and temporal compressions
With increasing climate simulation data volumes, the conventional web-based data retrieval method suffers from three limitations while retrieving large data volumes, similar to those experienced when using the d4PDF
Summary
In the field of climate research, improvements in computer performances have led to significant growths in the volumes of large ensemble simulation data. The volumes are estimated to exceed ∼ 3 PB in the case of the database for Policy Decision making for Future climate change (d4PDF; Mizuta et al 2017), which is produced by the Program for Risk Information on Climate Change. Systematic analyses of such large ensemble simulation data are relatively useful for the projection of probabilistic effects of climate change for extreme weather events. The SI-CAT project is intended to help local governments by promoting developments in adaptation plans and by assisting companies to create new businesses based on climate change adaptation needs
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