Review on Data Analytics for Climate Studies
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2018 by IJETT Journal | ||
| Volume-66 Number-1 |
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| Year of Publication : 2018 | ||
| Authors : Mrs. Radhika T V, K C Gouda, S.Sathish Kumar |
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| DOI : 10.14445/22315381/IJETT-V66P206 |
Citation
MLA Style: Mrs. Radhika T V, K C Gouda, S.Sathish Kumar "Review on Data Analytics for Climate Studies" International Journal of Engineering Trends and Technology 66.1 (2018): 30-37.
APA Style:Mrs. Radhika T V, K C Gouda, S.Sathish Kumar (2018). Review on Data Analytics for Climate Studies. International Journal of Engineering Trends and Technology, 66(1), 30-37.
Abstract
Climate system comprises of wide range of complex interactions and interrelationships occurring over time. Climate change is pervasive and may cause huge impact on society leading to financial and economic losses. As climate data is huge because of so many parameters at global spatial scale and hundreds of years in temporal scale so it is considered to be Big Data. Processing of these data for analysis and analytics requires High Performance Computing (HPC platform), which is achieved through big monster machines i.e. “Super Computers”. Much of the cost and time is spent in effectively managing data in such machines. So currently there is an urge to address these issues with the help of distributed computing facilities deployed in cloud environment. In a cloud platform generally different users (researchers) requires the computational resource as well as the climate data for performing different task. Analysing climate data is considered to be one of the most challenging work now a days as it requires huge computing and robust algorithms and codes. In this paper we address different aspects of climate studies such as format of storing climate data, numerical weather prediction models, visualization tools, and database used for storing climate data. Finally we address general architecture of distributed computing platform deployed in cloud to give solutions to challenges faced in Climate Analytics. Understanding and analysing these aspects will be very much helpful for prioritizing user preferences, interest and thus helps to extract useful results based on specific criteria.
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Keywords
Super Computers, HPC, Distributed Computing, Future Prediction Models, SPARK, Climate Analytics.