Review on Data Analytics for Climate Studies
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Mrs. Radhika T V, K C Gouda, S.Sathish Kumar
|DOI : 10.14445/22315381/IJETT-V66P206|
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.
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.
 Gregory Giuliani., Stefano Nativi., Andre Obregon., Martin Beniston., Anthony Lehmann.: Spatially enabling the GlobalFramework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data & information, Journal of Climate Services, ScienceDirect, August 2017, http://dx.doi.org/10.1016/j.cliser.2017.08.003.
 Albert M.G., Klein Tank, Francis W. Zwiers, Xuebin Zhang,: Guidelines on Analysis of extremes in a changing Climate in support of informed decisions for adaptation, Research report, World Climate Data and Monitoring Programme (WCDMP) (2009), Report No. WCDMP-72.
 Subarna Bhattacharyya, Detelina Ivanova: Chapter 6-Scientific Computing and Big Data Analytics:Application in Climate Science, ebook on Distributed computing in big data analytics: concepts, technologies and applications, Springer (August 2017), ISBN 978-3-319-59834-5.
 Climate Data Management System Specification, World Meteorological Organization (WMO) 2014, WMO-No. 1131, ISBN: 978-92-63-11131-9.
 J.L.Schnase, Daniel Q Duffy, Glenn S Tamkin :MERRA Analytic Services: Meeting the Big Data challenges of climate science through cloud-enabled Climate Analytics-as-a-Service, Vol. 61, Journal of Computers, Environment and Urban Systems ,January 2017, Part B pp. 119-212.
 S.Fiore, A. D’Anca, C. Palazzo,I. Fosterc, D. N. Williamsd, G. Aloisio: Ophidia: toward big data analytics for eScience, International Conference on Computational Science, Vol. 18, Journal of Procedia Computer Science, 2013, 2376-2385
 Junhua Yang, Keqin Duan, Effects of Initial Drivers and Land Use on WRF Modeling for Near-Surface Fields and Atmospheric Boundary Layer over the North eastern Tibetan Plateau, Vol. 2016, Research Article-Advances in Meteorology, Article ID 7849249, Hindawi Publishing Corporation , 2015.
 Shiyuan Zhong, Hee-Jin In, Xindi Bian, Joseph Charney, Warren Heilman, Brian Potter: Ealuation of Real-Time High- Resolution MM5 Predictions over the Great Lakes Region, Vol. 20, Journal on Weather and forecasting , Feb 2005, pp. 63-81.
 Venkata Bhaskar Rao Dodla, Satyaban Bishoyi Ratna, SrinivasDesamsetti: An assessment of cumulus parameterization schemes in the short range prediction of rainfall during the onset phase of the Indian Southwest Monsoon using MM5 Model, Vol. 120–121, Journal of Atmospheric Research, Feb. 2013, pp. 249-26.
 T.Nocke, T. Sterzel, M.Bottinger, M. Wrobel: Visualization of Climate and Climate Change Data: An Overview, ResearchGate , 18 August 2014. https://www.researchgate.net/publication/241401725.
 https://developers.arcgis.com/documentation/core-concepts/what-is- arcgis/
 Guide to climatological practices-chapter 3: climate data management, World Meteorological Organization (WMO) (2011), WMO-No.100, ISBN 978-92-63-10100-6.
 Radhika.T.V, K.C. Gouda, S.Sathish Kumar: Big Data Research in climate science, International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, IEEE Acc. No.-16776459, 21-22 Oct. 2016 Oct. 2016.
Super Computers, HPC, Distributed Computing, Future Prediction Models, SPARK, Climate Analytics.