Spectral Analysis of Global Warming and its Forecasting using Haar Wavelet Transforms

摘要

Greenhouse gases of the earth atmosphere are responsible for warming it and make it liveable. Excess production of greenhouse gases raises the temperature of earth atmosphere and makes global warming as hazardous. Temperature is the scale of global warming and also a prime parameter to describe the climate of any country or place. Global warming is one of the greatest challenges before the government and scientists around the world. Wavelet transforms is a new and effective tool to analyze the non-stationary and transient signals. It provides both time and frequency localization of a signal. Stationary wavelet transforms overcome the lack of time invariance of discrete wavelet transforms and are very useful to extend a signal. The average temperature record of India from 1901-2019 is taken as raw data and extended up to next 50 years (2069). The highest scale approximation represents the trend or average behavior of any signal/data. The trends of both original and extended signals are obtained using discrete Haar wavelet transforms and compared. The spectral analysis of effects of global warming using Haar wavelet transforms reveals the continuous increase in average temperature of India with a slight declined rate. The statistical parameters like average, skewness and kurtosis of both original and extended data are determined and interpreted. The statistical analytical results of original and extended data provide strong consistency with the spectral analytical results of the signals.