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

Kim, Soojun; Noh, Huiseong; Kang, Narae; Lee, Keon-Haeng; Kim, Yonsoo; Lim, Sanghun; Lee, Dong Ryul; Kim, Hung Soo

doi:10.1155/2014/517571

Cited by 9 publications

(6 citation statements)

References 49 publications

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“…The Cholesky decomposition method is used to generate two or more correlated random numbers. In the case of a symmetric matrix (C), a lower triangular matrix can be used, such as Equation (8).…”

Section: Of 18mentioning

confidence: 99%

Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Lee

Kang

Joo

et al. 2019

Water

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The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

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Section: Of 18mentioning

confidence: 99%

Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Lee

Kang

Joo

et al. 2019

Water

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“…Here N = The number of data points. When the values evaluated in the confidence interval set by the researchers exceed the interval, the null hypothesis is rejected, and the data is judged to be nonlinear [38][39][40].…”

Section: Bds Statistic and Nonlinearity Testmentioning

confidence: 99%

On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Kim

Joo²,

Han

et al. 2019

Water

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Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics.

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“…To examine the relationship between noisy signals and their BDS statistic, the BDS statistic (the embedding dimensions is 5 33 ) of the four synthetic noisy signals with different SNRs was calculated. As shown in the left side of Table 1, the BDS statistic increases as SNR increases.…”

Section: Synthetic Signal Analysismentioning

confidence: 99%

Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

Yang

Chen

et al. 2018

Advances in Mechanical Engineering

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To obtain accurate computer numerical control lathe cutting force signals and improve the precision of load stress cycle statistic, an improved multi-wavelet denoising with neighboring coefficients method is proposed. First, statistical variance smoothing is applied to remove the singular points in the original signal. The processed signal is then denoised with the multi-wavelet with neighboring coefficients method. Second, based on the change laws of the correlation dimension and the values of Brock, Dechert, Scheinkman statistic of load signal, reasonable decomposition levels of the multi-wavelet and the length of neighboring coefficients are used. Third, four synthetic signals with different signal-to-noise ratios are denoised with the wavelet threshold denoising method and improved multi-wavelet denoising with neighboring coefficients. Then, the difference between the values of correlation dimension and Brock, Dechert, Scheinkman statistic in the original and denoised signals is analyzed. Meanwhile, its validity is further verified with the signal-to-noise ratio and mean square error. The results show that the improved multi-wavelet denoising with neighboring coefficients is better than wavelet threshold. Finally, turning force signals are denoised by wavelet threshold and improved multi-wavelet denoising with neighboring coefficients. Comparison result shows that the improved multi-wavelet denoising with neighboring coefficients can not only remain largely low-frequency signal energy and suppress high-frequency noise signals effectively but also improve the accuracy of load stress cycle statistic.

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

Cited by 9 publications

References 49 publications

Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Improved multi-wavelet denoising with neighboring coefficients of cutting force for application in the load spectrum of computer numerical control lathe

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