ABSTRACT:
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.
Cite this article:
Anil Kumar. Spectral Analysis of Global Warming and its Forecasting using Haar Wavelet Transforms. Research J. Science and Tech. 2020; 12(4):260-266. doi: 10.5958/2349-2988.2020.00035.2
Cite(Electronic):
Anil Kumar. Spectral Analysis of Global Warming and its Forecasting using Haar Wavelet Transforms. Research J. Science and Tech. 2020; 12(4):260-266. doi: 10.5958/2349-2988.2020.00035.2 Available on: https://rjstonline.com/AbstractView.aspx?PID=2020-12-4-5
REFERENCES:
1. Barry RG and Chorley RJ. Atmosphere, Weather and Climate. 9th ed. Routledge, New York. 2009.
2. Chellali F., Khellaf A. and Belouchrani A. Wavelet spectral analysis of the temperature and wind speed data at Adrar. Algeria Renewable Energy. 35; 2010: 1214–1219.
3. Saboohi R, Soltani S and Khodagholi M. Trend analysis of temperature parameters in Iran. Theoretical Applied Climatology. 109; 2012: 529–547.
4. Adamowski J, Adamowski K. and Prokoph A. A. spectral analysis-based methodology to detect climatological influences on daily urban water demand. Mathematical Geosciences. 45; 2013: 49–68.
5. De Lima MIP, Santo FE, Ramos AM and De Lima JLMP. 2013. Recent changes in daily precipitation and surface air temperature extremes in mainland Portugal in the period 1941–2007. Atmospheric Research. 127; 2013: 195–209.
6. Nalley D, Adamowski J, Khalil B and Ozga-Zielinski B. Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Atmospheric Research. 132; 2013: 375–398.
7. Kammler D. A first course of Fourier Analysis. Prentice-Hall, New Jersey. 2000.
8. Hernandez E and Weiss G. A First Course on Wavelets. CRC Press, New York. 1996.
9. Walnut D. An Introduction to Wavelet Analysis, Applied and Numerical Harmonic analysis. Birkhauser, Boston. 2001.
10. Daubechies I. The wavelet transforms time-frequency localization and signal analysis. IEEE Transaction Information Theory. 36; 1990: 961–1004.
11. Torrence C and Compo GP. 1998. A Practical Guide to Wavelet Analysis. Bulletin of American Meteorolgical Society. 79; 1998: 61–78.
12. Soman KPK and Ramchandran I. Insight into Wavelets: From Theory to Practice”, 2nd ed., Prentice Hall, India. 2005.
13. Mallat SG. A Wavelet Tour of Signal Processing. Academic Press, New York. 1998.
14. Coifman RR, Meyer Y and Wickerhauser MV. Wavelet analysis and signal processing. In Wavelets and Their Applications. Jones and Bartlett, Boston. 1992.
15. De Artigas MZ, Elias AG and De Campra PF. Discrete wavelet analysis to assess long-term trends in geomagnetic activity. Physics and Chemistry of Earth. 31; 2006: 77–80.
16. Nason GP and Silverman BW. The stationary wavelet transforms and some statistical applications. Wavelet and Statistics: Lecture Note in Statistics. Springer, New York. 103; 1995: 281-299.
17. Pratap R. Getting started with MATLAB7: A Quick Introduction for Scientists and Engineers. 2nd Edition, Oxford University Press, New York. 2006.
18. Coifman RR and Wickerhauser MV. Entropy based algorithms for best basis selection. IEEE Transaction Information Theory. 38(2); 1992: 713-718.
19. Rockinger M and Jondeau E. Entropy densities with an application to autoregressive conditional skewness and kurtosis. Journal of Econometrics. 106(1); 2002: 119-142.
20. Wilks DS. Statistical Methods in Atmospheric Science. 3rd Edition, Academic Press, USA. 2011.