Big Data Research in climate science

currently climate research is most priority area as climate change generally affects the society as a whole. So there is requirement to study the weather and climate variability at very high resolution in multiple spatial and temporal scales. Presently climate data are huge in size as more data are being generated compared to past. Also sophisticated models are being used for prediction of weather and climate variability, generating vast amount of multidimensional digital data. As such climate data is considered to be big data which is multi-dimensional, multi-approach and multi-source. So the demand of high performance computing and cloud computing is being increased to do the climate research. This paper gives an outline of a few strategies in supporting big data administration and investigation in geoscience domain for climate studies. By analyzing contemporary information technologies and approaches, it can confirm what operational program framework and approaches are at hand and pertinent in developing big data-driven climate research. A transitory overview of HBase for storing and managing notable geoscience data across distributed machinery is highlighted. Withal MapReduce-predicated techniques to fortify parallel access of massive NetCDF data are considered. The outcomes can recognize basic issues and enhances the proficiency of dissecting huge geoscience information by lessening information preparing time.