Investigation and Analysis of Sea Surface Temperature and Precipitation of the Southern Caspian Sea Using Wavelet Analysis

Most time series from real-world processes are stained with noise. Therefore, much attention should be paid to data noise removal techniques. In this study, we use the family of biorthogonal wavelet, high-pass, and low-pass filters, to investigate the power of the wavelet method in removing noise from time series data. Using the wavelet discrete transformation, the variability of precipitation and sea surface temperature is analyzed for a southern region of the Caspian Sea. At each stage of decomposition, the previous wave is decomposed into two waves. In this research, the SST and precipitation data are decomposed into several levels based on discrete wavelet transformation. In each level of decomposition, the previous wave is decomposed into two waves. This can be done many times and at each stage, reducing the amount of data. This method is reversible, and the original wave can be reconstructed using the decomposed values. In the study of discrete wavelet transforms, it was observed that the analysis based on wavelets leads to more accurate results. The reconstruction error in the proposed method is shown to be very small.

Automated summary

Most time series from real-world processes are stained with noise, so it is important to focus on data noise removal techniques. In this study, the power of the wavelet method in removing noise from time series data is investigated using the family of biorthogonal wavelet, high-pass, and low-pass filters. The variability of precipitation and sea surface temperature in a southern region of the Caspian Sea is analyzed using wavelet discrete transformation. The previous wave is decomposed into two waves at each stage of decomposition, reducing the amount of data.