Improving the understanding of the influencing factors on sea level based on wavelet coherence and partial wavelet coherence

Relationship between sea level change and a single climate indicator has been widely discussed.However,few studies focused on the relationship between monthly mean sea level(MMSL)and several key impact factors,including CO_(2) concentration,sea ice area,and sunspots,on various time scales.In addition,research on the independent relationship between climate factors and sea level on various time scales is lacking,especially when the dependence of climate factors on Nino 3.4 is excluded.Based on this,we use wavelet coherence(WC)and partial wavelet coherence(PWC)to establish a relationship between MMSL and its influencing factors.The WC results show that the influence of climate indices on MMSL has strong regional characteristics.The significant correlation between Southern Hemisphere sea ice area and MMSL is opposite to that between Northern Hemisphere sea ice area and MMSL.The PWC results show that after removing the influence of Nino 3.4,the significant coherent regions of the Pacific Decadal Oscillation(PDO),Dipole Mode Index(DMI),Atlantic Multidecadal Oscillation(AMO),and Southern Oscillation Index(SOI)decrease to varying degrees on different time scales in different regions,demonstrating the influence of Nino 3.4.Our work emphasizes the interrelationship and independent relationship between MMSL and its influencing factors on various time scales and the use of PWC and WC to describe this relationship.The study has an important reference significance for selecting the best predictors of sea level change or climate systems.

The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation

The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.

Co‑movement of commodity price indexes and energy price index:a wavelet coherence approach

This research sheds light on the causal link between commodity price indexes,i.e.,the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index,in the global market,using wavelet coherence,Toda–Yamamoto causality,and gradual shift causality tests over the period 1992M1 to 2019M12.Findings from the wavelet power spectrum and partial wavelet coherence reveal that:(1)there was significant volatility in the Agricultural Raw Materials Price Index,Industry Input Price Index,Metal Price Index,and Energy Price Index between 2004 and 2014 at different frequencies;and(2)commodity price indexes significantly caused the energy price index at different time periods and frequencies.It is noteworthy that the outcomes of the Toda–Yamamoto causality and gradual-shift causality tests are in line with the results of wavelet coherence.

Triadic Synchrony: Application of Multiple Wavelet Coherence to a Small Group Conversation

By applying multiple wavelet coherence (MWC) to data from human body movements in triadic interaction, this study quantified triadic synchrony, rhythmic similarity among three interactants. Thirty-nine Japanese undergraduates were randomly assigned in a triad, and engaged in a brain-storming task. Triadic synchrony was quantified by calculating MWC to the time-series movement data collected by Kinect v2 sensor. The existence of synchrony was statistically tested by using a pseudo-synchrony paradigm. Results showed that the averaged value of MWC was higher in the experimental participant trio than in those of the pseudo trio in the frequency band of 0.5 - 1 Hz. The result supports the possible utility of applying multiple wavelet coherence to evaluate triadic synchrony in a small group interaction.

Synergistic effects of multiple driving factors on the runoff variations in the Yellow River Basin,China

River runoff plays an important role in watershed ecosystems and human survival,and it is controlled by multiple environmental factors.However,the synergistic effects of various large-scale circulation factors and meteorological factors on the runoff on different time-frequency scales have rarely been explored.In light of this,the underlying mechanism of the synergistic effects of the different environmental factors on the runoff variations was investigated in the Yellow River Basin of China during the period 1950-2019 using the bivariate wavelet coherence(WTC)and multiple wavelet coherence(MWC)methods.First,the continuous wavelet transform(CWT)method was used to analyze the multiscale characteristics of the runoff.The results of the CWT indicate that the runoff exhibited significant continuous or discontinuous annual and semiannual oscillations during the study period.Scattered inter-annual time scales were also observed for the runoff in the Yellow River Basin.The meteorological factors better explained the runoff variations on seasonal and annual time scales.The average wavelet coherence(AWC)and the percent area of the significant coherence(PASC)between the runoff and individual meteorological factors were 0.454 and 19.89%,respectively.The circulation factors mainly regulated the runoff on the inter-annual and decadal time scales with more complicated phase relationships due to their indirect effects on the runoff.The AWC and PASC between the runoff and individual circulation factors were 0.359 and 7.31%,respectively.The MWC analysis revealed that the synergistic effects of multiple factors should be taken into consideration to explain the multiscale characteristic variations of the runoff.The AWC or MWC ranges were 0.320-0.560,0.617-0.755,and 0.819-0.884 for the combinations of one,two,and three circulation and meteorological factors,respectively.The PASC ranges were 3.53%-33.77%,12.93%-36.90%,and 20.67%-39.34%for the combinations one,two,and three driving factors,respectively.The combinations of precipitation,evapotranspiration(or the number of rainy days),and the Arctic Oscillation performed well in explaining the variability in the runoff on all time scales,and the average MWC and PASC were 0.847 and 28.79%,respectively.These findings are of great significance for improving our understanding of hydro-climate interactions and water resources prediction in the Yellow River Basin.

The influence of human activity and precipitation change on mid-long term evolution of landslide and debris flow disasters

After defining landslide and debris flow, human activity, and precipitation indices, using with landslide and debris flow disaster data in low-latitude plateau of China, reflecting human activity and precipitation data, the influence of human activity and precipitation on mid-long term evolution of landslide and debris flow was studied with the wavelet technique. Results indicate that mid-long evolution of landslide and debris flow disaster trends to increase 0.9 unit every year, and presents obvious stage feature. The abrupt point from rare to frequent periods took place in 1993. There is significant in-phase resonance oscillation between human activity and landslide and debris flow frequency on a scale of 11-16 years, in which the variation of human activity occurs about 0.2-2.8 years before landslide and debris flow variation. Thus, the increase of landslide and debris flow frequency in low latitude plateau of China may be mainly caused by geo-environmental degradation induced by human activity. After the impact of human activity is removed, there is sig- nificant in-phase resonance oscillation between landslide and debris flow frequency and summer rainfall in low-latitude plateau of China in quasi-three-year and quasi-six-year scales, in which the variation of summer precipitation occurs about 0.0-0.8 years before landslide and debris flow variation. Summer precipitation is one of important external causes which impacts landslide and debris flow frequency in low-latitude plateau of China. The mid-long term evolution predicting model of landslide and debris flow disasters frequency in low-latitude plateau region with better fitting and predicting ability was built by considering human activity and summer rainfall.

DETECTING BILATERAL FUNCTIONAL CONNECTIVITY IN THE PREFRONTAL CORTEX DURING A STROOP TASK BY NEAR-INFRARED SPECTROSCOPY

Near infrared spectroscopy(NIRS)is generally accepted as a functional brain imaging technology for brain activation study.With multichannel highly sensitive NIRS instruments,it has become possible to assess functional connectivity of different brain negions by NIRS.However,the feasibility needs to be validated in complex cognitive activities.In this study,we recorded the hemodynamic activity of the bilateral prefrontal cortex(PFC)during a color-word matching Stroop task.Wavelet transform coberence(WTC)analysis was applied to ascss the functional conectivity of all homologous channel pairs within the left/right PFC.Both the behavioral and brain activ ation results showed signifcant Stroop effects.The results of WTC analysis revealed that,bilateral functional connectivity was significantly stronger during both the incongruent stimuli and neutral stimuli compared to that of the rest period.It also showed significant Stroop effect.Our findings demonstrate that,NIRS bcomes a valuable tool to elucidate the functional conectivity of brain cortex in complex cognitive activities.

Modeling and forecasting time series of precious metals:a new approach to multifractal data

We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified scale.First,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence analysis.Then,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are calculated.Additionally,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent calculations.These internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted accordingly.In our study,the daily prices of gold,silver and platinum are used for assessment.The results have shown that all metals do behave in phase movement on long term periods and possess multifractal features.Furthermore,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA model.Conclusively,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in modeling.Our study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen before.The results will be of particular significance to finance researchers and practitioners.

The meaning of “coherent” and its quantication in coherent hemodynamics spectroscopy

We have recently introduced a new technique,coherent hemodynamics spectroscopy(CHS),which aims at characterizing a specic kind of tissue hemodynamics that feature a high level of covariation with a given physiological quantity.In this study,we carry out a detailed analysis of the signicance of coherence and phase synchronization between oscillations of arterial blood pressure(ABP)and total hemoglobin concentration([Hbt]),measured with near-infrared spectroscopy(NIRS)during a typical protocol for CHS,based on a cyclic thigh cuffocclusion and release.Even though CHS is based on a linear time invariant model between ABP(input)and NIRS measurands(outputs),for practical reasons in a typical CHS protocol,we inducenite“groups”of ABP oscillations,in which each group is characterized by a different frequency.For this reason,ABP(input)and NIRS measurands(output)are not stationary processes,and we have used wavelet coherence and phase synchronization index(PSI),as a metric of coherence and phase synchronization,respectively.PSI was calculated by using both the wavelet cross spectrum and the Hilbert transform.We have also used linear coherence(which requires stationary process)for comparison with wavelet coherence.Themethod of surrogate data is used tond critical values for the signicance of covariation between ABP and[Hbt].Because we have found similar critical values for wavelet coherence and PSI by usingve of the most used methods of surrogate data,we propose to use the data-independent Gaussian random numbers(GRNs),for CHS.By using wavelet coherence and wavelet cross spectrum,and GRNs as surrogate data,we have found the same results for the signicance of coherence and phase synchronization between ABP and[Hbt]:on a total set of 20 periods of cuffoscillations,we have found 17 coherent oscillations and 17 phase synchronous oscillations.Phase synchronization assessed with Hilbert transform yielded similar results with 14 phase synchronous oscillations.Linear coherence and wavelet coherence overall yielded similar number of signicant values.We discuss possible reasons for this result.Despite the similarity of linear and wavelet coherence,we argue that wavelet coherence is preferable,especially if one wants to use baseline spontaneous oscillations,in which phase locking and coherence between signals might be only temporary.

Wavelet-based estimation of EEG synchronization associated with native and second language processing in Stroop task

Objective:To examine and compare the synchronization of different brain regions during the Chinese and English Stroop tasks.Methods.Ten native Chinese speakers with a moderate command of English participated in this study,and event-related potentials were recorded while participants performed the Stroop task.Then wavelet-based estimation of instantaneous EEG coherence was applied to investigate the synchronization of different brain regions during Stroop task.Results:A greater negativity for the in- congruent situation than congruent situation appeared from 350ms to 600ms post-stimulus onset over frontal,central,and parietal regions in Chinese Stroop task,while the negativity was absent in English Stroop task.However,not only in Chinese Stroop task but also in English Stroop task was it found signif- icantly higher EEG coherences for the incongruent situation than congruent situation over frontal,pari- etal,and frontoparietal regions before 400ms post stimulus onset atβ(13-30 Hz) frequency band.Conclu- sion:This finding indicated that wavelet-based coherence is more exquisite tool to analyze brain electro- physiological signals associated with complex cognitive task than ERP component,and that functional syn- chronization indexed by EEG coherence is enhanced at the earlier stage while processing the conflicting in- formation for the incongruent stimulus.

Development of a validation technique for road surface profile applicable to point cloud data

In recent years,various information and communication technology(ICT)devices measuring three-dimensional(3D)point cloud data have been developed and widely used for the application of pavement surface investigation.However,ICT devices have generally been developed not only for measuring road surface profiles but for various geo-reference point clouds.In this background,the validation of surface profiles acquired with ICT devices fulfils an important role in proving the reliability of measurement result composed by point clouds.This study proposes a wavelet transform agreement(WTA)which involves a normalization factor of profile amplitude for further improvement in the wavelet-based coherence technique.The WTA analysis allows evaluating similarity/dissimilarity of two profiles considering both the location and wavelength simultaneously.For this purpose,a terrestrial laser scanner(TLS),a mobile mapping system(MMS),and an unmanned aerial vehicle(UAV)are employed to prove the advantage of WTA in practical applications.As a result,the advantages of WTA analysis are clearly recognized in the optimization for the measurement interval of TLS,the multi-line measurement of MMS for ride quality improvement of a pavement,and the efficient operation of UAV in terms of the flight altitude.This paper also shows the identification of aging development for surface roughness over time in terms of locations and wavelengths.These findings help not only to guarantee the accuracy of profile measurements but to realize the sophisticated way of using 3D point clouds acquired with ICT devices.The outcomes of this study contribute to the increase of productivity for pavement works with improving the quality of surface profile measurement.

Spatio-temporal patterns of temperature extremes and their response to atmospheric circulation factors in China from 1961 to 2020

Changes in surface temperature extremes have become a global concern.Based on the daily lowest temperature(TN)and daily highest temperature(TX)data from 2138weather stations in China from 1961 to 2020,we calculated 14 extreme temperature indices to analyze the characteristics of extreme temperature events.The widespread changes observed in all extreme temperature indices suggest that China experienced significant warming during this period.Specifically,the cold extreme indices,such as cold nights,cold days,frost days,icing days,and the cold spell duration index,decreased significantly by-6.64,-2.67,-2.96,-0.97,and-1.01 days/decade,respectively.In contrast,we observed significant increases in warm extreme indices.The number of warm nights,warm days,summer days,tropical nights,and warm spell duration index increased by 8.44,5.18,2.81,2.50,and 1.66d/decade,respectively.In addition,the lowest TN,highest TN,lowest TX,and highest TX over the entire period rose by 0.47,0.22,0.26,and 0.16℃/decade,respectively.Furthermore,using Pearson's correlation and wavelet coherence analyses,this study identified a strong association between extreme temperature indices and atmospheric circulation factors,with varying correlation strengths and resonance periods across different time-frequency domains.

Time-varying nexus and causality in the quantile between Google investor sentiment and cryptocurrency returns

Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.

Who Plays the Key Role among Shanghai,Shenzhen and Hong Kong Stock Markets?

In this paper we examine the daily frequency stock market indices of Shanghai, Shenzhen and Hong Kong from January 2000 to June 2012, and use the Morlet wavelet coherence model to determine who is playing the most important role in the financial markets of China. We find that there are significant comovements between these stock markets in the medium and long run. This provides investors with opportunities to increase their capital gains. The Hong Kong stock market plays a leading role in the long run, but its leader position is threatened by fast-growing Chinese mainland stock markets, especially the Shanghai Stock Exchange. Based on our analysis, the following suggestions apply to the Chinese stock markets： establish and improve international and regional finance centers in Chinese mainland; encourage more qualified institutional investors; reposition the market relations among Hong Kong, Shanghai and Shenzhen; and increase deregulation and internationalization to speed up the integration of financial resources.

Scale-location specific relations between soil nutrients and topographic factors in the Fen River Basin, Chinese Loess Plateau

Understanding scale-and location-specific variations of soil nutrients in cultivated land is a crucial consideration for managing agriculture and natural resources effectively. In the present study, wavelet coherency was used to reveal the scale-location specific correlations between soil nutrients, including soil organic matter （SOM）, total nitrogen （TN）, available phosphorus （AP）, and available potassium （AK）, as well as topo- graphic factors （elevation, slope, aspect, and wetness index） in the cultivated land of the Fen River Basin in Shanxi Province, China. The results showed that SOM, TN, AP, and AK were significantly inter-correlated, and that the scales at which soil nutrients were correlated differed in different landscapes, and were generally smaller in topographically rougher terrain. All soil nutrients but TN were significantly influenced by the wetness index at relatively large scales （32-72 km） and AK was significantly affected by the aspect at large scales at partial locations, showing localized features. The results of this study imply that the wetness index should be taken into account during farming practices to improve the soil nutrients of cultivated land in the Fen River Basin at large scales.

Warming hiatus of extreme temperature across China's cold regions during 1998-2018

The recent hiatus in global warming has attracted significant attention,yet whether it is a widespread global and/or regional phenomenon remains controversial.Here,we investigate the response of extreme temperature changes since 1961 across China's cold regions(CCR):Tibetan determine the spatiotemporal characteristics of extreme temperature changes across these cold regions using Mann-Kendall and wavelet transform coherence(WTC)analyses of data from 196 meteorological stations from 1961 to 2018.We further investigate the teleconnection between extreme temperatures and large-scale ocean-atmosphere circulation to determine the potential synoptic scale causes of the observed changes.The results revealed a significant warming slowdown in all extreme tempera-ture indices across CCR from 1998 to 2018.In addition,extreme temperature indices in northwest cold region(NWC)and north cold region(NC)reveal a clear winter warming slowdown and even a significant cooling trend,yet only the cold index in Tibetan Platean cold region(TPC)shows a warming hiatus.We conclude that the warming hiatus observed across these regions is primarily driven by extreme temperature index changes in winter.We also find that phase variations in the Atlantic Multi-decadal Oscillation(AMO)and Arctic Oscillation(AO)critically impact on the observed warming hiatus,but the specific atmospheric mechanisms are elusive and warrant further analysis and investigation.