Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2014 (2014), Article ID 517571, 10 pages
Research Article

Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

1Columbia Water Center, Columbia University, New York, NY 10027, USA
2Department of Civil Engineering, Inha University, Incheon 402-751, Republic of Korea
3Water Resources Research Division, Korea Institute of Civil Engineering and Building Technology (KICT),Goyang 411-712, Republic of Korea

Received 1 July 2014; Accepted 29 July 2014; Published 20 August 2014

Academic Editor: Vincenzo Levizzani

Copyright © 2014 Soojun Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.