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EN
Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials.
EN
Severe amplitude and phase scintillation induced by the ionospheric plasma density irregularities degrades the performance of global navigation satellite system (GNSS) receivers. Scintillation typically has adverse effects at the tracking process and thus adversely affects the raw GNSS measurements used in a number of applications. Hence, it is important to develop robust methodologies for detecting and mitigating ionospheric effects on the GNSS signals. In this paper, we propose a novel method based on the combination of improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN) and variational mode decomposition (VMD) methods. The proposed method employs a detrended fuctuation analysis (DFA)-based metric for robust thresholding between the scintillation-free and amplitude scintillated GNSS signals. The major contribution of the proposed method is development of novel approaches for selection of intrinsic mode functions (IMFs) based on DFA and optimised selection of [K, 훼] parameters of the VMD. The performance of the proposed method was evaluated and was observed that it is better than existing ionospheric scintillation effects mitigation algorithms for both simulated and real-time GPS scintillation datasets. The proposed method can denoise approximately 9.23–15.30 dB scintillation noise from the synthetic and 0.2–0.48 from the real scintillation index (S4) values. Therefore, the proposed (iCEEMDAN-VMD) method is appropriate for mitigating the ionospheric scintillation effects on the GNSS signals.
3
Content available remote Detection of ionospheric scintillation effects using LMD–DFA
EN
The performance and measurement accuracy of global navigation satellite system (GNSS) receivers is greatly affected by ionospheric scintillations. Rapid amplitude and phase variations in the received GPS signal, known as ionospheric scintillation, affects the tracking of signals by GNSS receivers. Hence, there is a need to investigate the monitoring of various activities of the ionosphere and to develop a novel approach for mitigation of ionospheric scintillation effects. A method based on Local Mean Decomposition (LMD)–Detrended Fluctuation Analysis (DFA) has been proposed. The GNSS data recorded at Koneru Lakshmaiah (K L) University, Guntur, India were considered for analysis. The carrier to noise ratio (C/N0) of GNSS satellite vehicles were decomposed into several product functions (PF) using LMD to extract the intrinsic features in the signal. Scintillation noise was removed by the DFA algorithm by selecting a suitable threshold. It was observed that the performance of the proposed LMD–DFA was better than that of empirical mode decomposition (EMD)–DFA.
EN
In this paper we apply the Asymmetric Detrended Fluctuation Analysis to the RR intervals time series. The mathematical background of the ADFA method is discussed in the context of heart rate variability and heart rate asymmetry. We calculate the α- and α+ ADFA scaling exponents for 100 RR intervals time series recorded in a group of healthy volunteers (20-40 years of age) with the use of the local ADFA. It is found that on average α+ < α-, and that locally α- dominates most of the time over α+ - both results are highly statistically significant.
EN
We investigate the time dynamics of monthly rainfall series intermittently recorded on seven climatic stations in northern Lebanon from 1939 to 2010 using the detrending fluctuation analysis (DFA) and the Fisher-Shannon (FS) method. The DFA is employed to study the scaling properties of the series, while the FS method to analyze their order/organization structure. The obtained results indicate that most all the stations show a significant persistent behavior, suggesting that the dynamics of the rainfall series is governed by positive feedback mechanisms. Furthermore, we found that the Fisher Information Measure (the Shannon entropy power) seems to decrease (increase) with the height of the rain gauge; this indicates that the rainfall series appear less organized and less regular for higher-located stations. Such findings could be useful for a better comprehension of the climatic regimes governing northern Lebanon.
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