Seasonal variability in climate time series in Rajshahi division, Bangladesh
Keywords:Hilbert transform, dry season, wet season, seasonal variability, fourier transform, seasonal boundary
This work has presented yearly dry and wet seasons in the analysis of 28 years daily recorded temperature, relative humidity and rainfall data from 1988 to 2015 in Rajshahi division, Bangladesh using Hilbert frequency analysis. Analysis has estimated the seasonal boundaries in time according to the instantaneous frequency in cycles/day and the estimations are verified with studying power spectrum of the time series. Two boundaries are obtained in each analysis over the average of yearly analysis of four years. Obtained seasonal boundaries on 16 March and 20 October are indicated as the differentiator of wet season comprises of pre-monsoon and rain in each year. Results have also shown that the length of the wet season is varying ±11days. Estimations have further justified with average rainfall distribution as shown in this work. It is even difficult to differentiate rainy season in rainfall data, however, the estimated wet season using Hilbert analysis well supported the rainy season over temperature and humidity. The presented analysis may assist further to learn more about the seasonal variability in climate dynamics.
Abdullah H.M., Rahman M.M., 2015. Initiating rainwater harvest technology for climate change induced drought resilient agriculture: Scope and challenges in Bangladesh. Journal of Agriculture and Environment for International Development, 109(2), 189–208.
Bartels R.H., Beatty J.C. and Barsky B A., 1998. "Hermite and Cubic Spline Interpolation." Ch. 3 in An Introduction to Splines for Use in Computer Graphics and Geometric Modelling. San Francisco, CA: Morgan Kaufmann, 9–17.
Duffy D.G., 2004. The Application of Hilbert–Huang Transforms to Meteorological Data Sets April 2004, Journal of Atmospheric and Oceanic Technology, 21(4): 599–611. Doi: 10.1175/1520-0426(2004)021<0599: TAOHTT>2.0.CO;2.
Harmeling S., Eckstein D., 2012. Global Climate Risk Index 2013. Who Suffers Most from Extreme Weather Events? Weather-Related Loss Events in 2011 and 1992 to 2011. German Watch, Bonn and Berlin, Germany, 28p.
Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen, N.C., Tung C.C., Liu H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Proc. R. Soc. London, Ser. A(454), 903–993.
Kreft S., Eckstein D., Melchior I., 2015. Global Climate Risk Index, 2017. Who Suffers Most from Extreme Weather Events? Weather-related Loss Events in 2015 and 1996 to 2015, Germanwatch e.V., Bonn, 32p.
Pan J.Y., Yan X.H., Zheng Q., Liu, W.T. and Klemas, V.V., 2003. Interpretation of scatter meter ocean surface wind vector EOFs over the northwestern Pacific, Remote Sens. Environ., 84, 53–68. Doi:10.1016/S0034-4257(02)00073-1.
Wan S.Q., Feng G.L., Dong W.J., Li J.P., Gao X.Q., He W.P., 2005. On the climate prediction of nonlinear and nonstationary time series with the EMD method, Chin. Phys., 14, 628–633. Doi:10.1088/1009-1963/14/3/036.
World Bank, 2010. Bangladesh Country assessment Strategy: 2011-2014. http://siteresources.worldbank.org/Bangladeshextn/Resources/295759-1271081222839/6958908-1284576442742/Bdcasfinal.pdf. Accessed February 2011, retrieved on January, 2019. http://siteresources.worldbank.org/Bangladeshextn/Resources/295759-1271081222839/6958908-1284576442742/Bdcasfinal.pdf. Accessed February 2011, retrieved on January, 2019.">
Wu Z., Huang N.E., 2005. Statistically significant test of intrinsic mode functions, in Hilbert-Huang Transform: Introduction and Applications, (eds) N. E. Huang and S.S.P. Shen, World Sci., Singapore, 125–148.
Xie L., Pietrafesa L.J., Wu K.J., 2002. Interannual and decadal variability of landfalling tropical cyclones in the southeast coastal states of the United States, Adv. Atmos. Sci., 19, 677–686. Doi:10.1007/s00376-002-0007-y.
Yan X.H., Zhou Y., Pan J., Zheng D., Fang M., Liao X., He M.X., Liu W.T., Ding, X., 2002. Pacific warm pool excitation, Earth rotation and El Nin˜o southern oscillations, Geophys. Res. Lett., 29(21), 271–274. Doi:10.1029/2002GL015685.