| Analysis of the precipitation climate signal using Empirical Mode Decomposition (EMD) |
| Paper ID : 1360-SMPR |
| Authors: |
|
Farideh Sabzehee *1, Vahab Nafisi2, Siavash Iran Pour3, Bramha Dutt Vishwakarma4 1Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran 2Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran. 3Guest Researcher, Institute of Geodesy, University of Stuttgart, Stuttgart, Germany. 4Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK |
| Abstract: |
| In this paper, we employ Empirical Mode Decomposition (EMD) together with Hilbert Transform to analyze precipitation time series. Several studies have shown that EMD can handle nonlinear and non-stationary signals by decomposing them into a finite number of Intrinsic Mode Functions (IMFs) in the time-frequency domain. While EMD, Fast Fourier Transform (FFT) and Wavelet Transform are all used to decompose time series, EMD is fundamentally different from other separation methods. In spite of the remarkable performance of the FFT approach, it can be noted that there are some limitations such as the inability of nonstationary signal processing and the lack of time transparency. The Wavelet Transform approach is failed to detect the instantaneous frequencies and need to have knowledge from data. The EMD has the ability to determine the signal characteristics no assumption and to estimate the instantaneous frequencies of the signal. The EMD is applied to identify the main frequencies of precipitation time series. Thereafter a statistical procedure is used to identify the prominent IMF of the original signal. |
| Keywords: |
| Empirical Mode Decomposition, Precipitation, Intrinsic mode functions, Trend, Hilbert Transform, Frequency. |
| Status : Conditional Accept (Poster) |
