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Data Analysis and Visualization in Python for Polar Meteorological Data

Data Analysis and Visualization in Python for Polar Meteorological Data
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Author(s): V. Sakthivel Samy (National Centre for Polar and Ocean Research, India), Koyel Pramanick (Pondicherry University, India), Veena Thenkanidiyoor (National Institute of Technology, Goa, India) and Jeni Victor (Indian Institute of Tropical Meteorology, India)
Copyright: 2021
Volume: 2
Issue: 1
Pages: 29
Source title: International Journal of Data Analytics (IJDA)
Editor(s)-in-Chief: Bruce Sun (SUNY Buffalo State, USA)
DOI: 10.4018/IJDA.2021010102

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Abstract

The aim of this study is to analyze meteorological data obtained from the various expeditions made to the Indian stations in Antarctica over recent years and determine how significantly the weather has shown a marked change over the years. For any time series data analysis, there are two main goals: (a) the authors need to identify the nature of the phenomenon from the sequence of observations and (b) predict the future data. On account of these goals, the pattern in the time series data and its variability are to be accurately identified. This paper can then interpret and integrate the pattern established with its associated meteorological datasets collected in Antarctica. Using the data analytics knowledge the validity of interpretation for the given datasets a pattern has been identified, which could extrapolate the pattern towards prediction. To ease the time series data analysis, the authors developed online meteorological data analytic portal at NCPOR, Goa http://data.ncaor.gov.in/.

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