Application of the cross wavelet transform and wavelet coherence to geophysical time series
Open accessAbstract
Abstract. Many scientists have made use of the wavelet method in analyzing time series, often using popular free software. However, at present there are no similar easy to use wavelet packages for analyzing two time series together. We discuss the cross wavelet transform and wavelet coherence for examining relationships in time frequency space between two time series. We demonstrate how phase angle statistics can be used to gain confidence in causal relationships and test mechanistic models of physical relationships between the time series. As an example of typical data where such analyses have proven useful, we apply the methods to the Arctic Oscillation index and the Baltic maximum sea ice extent record. Monte Carlo methods are used to assess the statistical significance against red noise backgrounds. A software package has been developed that allows users to perform the cross wavelet transform and wavelet coherence (www.pol.ac.uk/home/research/waveletcoherence/).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Baltosser, W. H., & Zar, J. H. (1996) · Ecology · doi:10.2307/2265725 · Cited by 35,166 · see citing works
The Arctic oscillation signature in the wintertime geopotential height and temperature fields
Thompson, D. W. J., & Wallace, J. M. (1998) · Geophysical Research Letters · doi:10.1029/98gl00950 · Cited by 4,158 · see citing works
The quasi‐biennial oscillation
Baldwin, M., Gray, L. J., Dunkerton, T. J., Hamilton, K., Haynes, P., Randel, W. J., Holton, J. R., Alexander, M. J., Hirota, I., Horinouchi, T., et al. (2001) · Reviews of Geophysics · doi:10.1029/1999rg000073 · Cited by 2,119 · see citing works
Jevrejeva, S., Moore, J. C., & Grinsted, A. (2003) · Journal of Geophysical Research Atmospheres · doi:10.1029/2003jd003417 · Cited by 412 · see citing works
Self-citation(1994) · Wavelet analysis and its applications · doi:10.1016/c2009-0-21241-4 · Cited by 376 · see citing works
Simulation of the Tropical Oceans with an Ocean GCM Coupled to an Atmospheric Mixed-Layer Model
Murtugudde, R. G., Seager, R., & Busalacchi, A. J. (1996) · Journal of Climate · doi:10.1175/1520-0442(1996)009 · Cited by 159 · see citing works
Influence of atmospheric circulation on the maximum ice extent in the Baltic Sea
Omstedt, A., & Chen, D. (2001) · Journal of Geophysical Research Atmospheres · doi:10.1029/1999jc000173 · Cited by 139 · see citing works
Jevrejeva, S., & Moore, J. C. (2001) · Geophysical Research Letters · doi:10.1029/2001gl013573 · Cited by 55 · see citing works
Self-citationLoewe, P., & Koslowski, G. (1998) · Tellus A Dynamic Meteorology and Oceanography · doi:10.3402/tellusa.v50i2.14522 · Cited by 28 · see citing works
Loewe, P., & Koslowski, G. (1998) · Tellus A Dynamic Meteorology and Oceanography · doi:10.1034/j.1600-0870.1998.t01-1-00005.x · Cited by 21 · see citing works
Jevrejeva, S. (2002) · Hydrology research · doi:10.2166/nh.2002.0011 · Cited by 17 · see citing works
Self-citationCarretero (1998) · doi:10.1175/1520-0477(1998)079
Latif, M. (1999) · doi:10.1175/1520-0442(1999)012
Flagged first, then by citations
2 of the 15 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Westerhold, T., Marwan, N., Drury, A. J., Liebrand, D., Agnini, C., Anagnostou, E., Barnet, J. S. K., Bohaty, S. M., Vleeschouwer, D. D., Florindo, F., et al. (2020) · Science · doi:10.1126/science.aba6853 · Cited by 2,175 · see citing works
Network modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878 · see citing works
Time–frequency dynamics of resting-state brain connectivity measured with fMRI
Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808 · see citing works
What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis
Krištoufek, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123923 · Cited by 1,008 · see citing works
International comovement of stock market returns: A wavelet analysis
Rua, A., & Nunes, L. C. (2009) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2009.02.002 · Cited by 845 · see citing works
Cui, X., Bryant, D. M., & Reiss, A. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.003 · Cited by 807 · see citing works
THE CONTINUOUS WAVELET TRANSFORM: MOVING BEYOND UNI‐ AND BIVARIATE ANALYSIS
Aguiar‐Conraria, L., & Soares, M. J. (2013) · Journal of Economic Surveys · doi:10.1111/joes.12012 · Cited by 722 · see citing works
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
Rhif, M., Abbes, A. B., Farah, I. R., Martínez, B., & Sang, Y. (2019) · Applied Sciences · doi:10.3390/app9071345 · Cited by 603 · see citing works
Using wavelets to decompose the time–frequency effects of monetary policy
Aguiar‐Conraria, L., Azevedo, N., & Soares, M. J. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.01.063 · Cited by 599 · see citing works
On the Spatio-Temporal Dynamics of Soil Moisture at the Field Scale
Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., & Vanderborght, J. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.11.061 · Cited by 584 · see citing works
The propagation from meteorological to hydrological drought and its potential influence factors
Huang, S., Li, P., Huang, Q., Leng, G., Hou, B., & Ma, L. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.01.041 · Cited by 560 · see citing works
Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis
Bouri, E. I., Shahzad, S. J. H., Roubaud, D., Krištoufek, L., & Lucey, B. M. (2020) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2020.03.004 · Cited by 527 · see citing works
Hyperscanning: A Valid Method to Study Neural Inter-Brain Underpinnings of Social Interaction
Czeszumski, A., Eustergerling, S., Lang, A., Menrath, D., Gerstenberger, M., Schuberth, S., Schreiber, F., Rendon, Z. Z., & König, P. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00039 · Cited by 511 · see citing works
Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis
Vácha, L., & Baruník, J. (2011) · Energy Economics · doi:10.1016/j.eneco.2011.10.007 · Cited by 498 · see citing works
Neural Synchronization during Face-to-Face Communication
Jiang, J., Dai, B., Peng, D., Zhu, C., Liu, L., & Lu, C. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2926-12.2012 · Cited by 489 · see citing works
Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis
Goodell, J. W., & Goutte, S. (2020) · Finance research letters · doi:10.1016/j.frl.2020.101625 · Cited by 483 · see citing works
Nguyen, T. T. H., Naeem, M. A., Balli, F., Balli, H. O., & Vo, X. V. (2020) · Finance research letters · doi:10.1016/j.frl.2020.101739 · Cited by 453 · see citing works
The NIRS Brain AnalyzIR Toolbox
Santosa, H., Zhai, X., Fishburn, F. A., & Huppert, T. J. (2018) · Algorithms · doi:10.3390/a11050073 · Cited by 442 · see citing works
Multifractal analysis of financial markets: a review
Zhi-qiang Jiang, A., Xie, W.-J., Zhou, W., & Sornette, D. (2019) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab42fb · Cited by 435 · see citing works
Spatiotemporal drought variability in the Mediterranean over the last 900 years
Cook, B. I., Anchukaitis, K. J., Touchan, R., Meko, D. M., & Cook, E. R. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd023929 · Cited by 438 · see citing works
Surrogate data for hypothesis testing of physical systems
Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V., & Stefanovska, A. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.06.001 · Cited by 425 · see citing works
Improving land surface models with FLUXNET data
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., et al. (2009) · Biogeosciences · doi:10.5194/bg-6-1341-2009 · Cited by 407 · see citing works
Stacking paleointensity and oxygen isotope data for the last 1.5 Myr (PISO-1500)
Channell, J. E. T., Xuan, C., & Hodell, D. A. (2009) · Earth and Planetary Science Letters · doi:10.1016/j.epsl.2009.03.012 · Cited by 393 · see citing works
Atlantic forcing of persistent drought in West Africa
Shanahan, T. M., Overpeck, J. T., Anchukaitis, K. J., Beck, J. W., Cole, J. E., Dettman, D. L., Peck, J. A., Scholz, C. A., & King, J. W. (2009) · Science · doi:10.1126/science.1166352 · Cited by 384 · see citing works
Nonlinear trends and multiyear cycles in sea level records
Jevrejeva, S., Grinsted, A., Moore, J. C., & Holgate, S. J. (2006) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jc003229 · Cited by 373 · see citing works
Self-citationZong, Q., Zhou, X., Wang, Y., Li, X.-M., Song, P., Baker, D. N., Fritz, T. A., Daly, P. W., Dunlop, M. W., & Pedersen, A. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009ja014393 · Cited by 366 · see citing works
Spatial and temporal characteristics of droughts in Central Asia during 1966–2015
Guo, H., Bao, A., Liu, T., Jiapaer, G., Ndayisaba, F., Jiang, L., Kurban, A., & Maeyer, P. D. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.12.120 · Cited by 363 · see citing works
Geophysical Applications of Partial Wavelet Coherence and Multiple Wavelet Coherence
Ng, E. K. W., & Chan, J. C. L. (2012) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-12-00056.1 · Cited by 347 · see citing works
Leader emergence through interpersonal neural synchronization.
Jiang, J., Chen, C., Dai, B., Shi, G., Ding, G., Liu, L., & Lu, C. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422930112 · Cited by 341 · see citing works
Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification
Banerjee, S., & Mitra, M. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2279001 · Cited by 336 · see citing works
A systematic framework for functional connectivity measures
Wang, H., Bénar, C., Quilichini, P., Friston, K. J., Jirsa, V., & Bernard, C. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00405 · Cited by 332 · see citing works
Time-dependent spectral analysis of epidemiological time-series with wavelets
Cazelles, B., Chávez, M., de Magny, G. C., Guégan, J., & Hales, S. (2007) · Journal of The Royal Society Interface · doi:10.1098/rsif.2007.0212 · Cited by 321 · see citing works
On the observed variability of monsoon droughts over India
Kumar, K. N., Rajeevan, M. N., Pai, D. S., Srivastava, A. K., & Preethi, B. (2013) · Weather and Climate Extremes · doi:10.1016/j.wace.2013.07.006 · Cited by 321 · see citing works
Cooperation in lovers: An fNIRS-based hyperscanning study
Pan, Y., Cheng, X., Zhang, Z., Li, X., & Hu, Y. (2016) · Human Brain Mapping · doi:10.1002/hbm.23421 · Cited by 313 · see citing works
Late Miocene climate cooling and intensification of southeast Asian winter monsoon
Holbourn, A., Kuhnt, W., Clemens, S. C., Kochhann, K. G. D., Jöhnck, J., Lübbers, J., & Andersen, N. (2018) · Nature Communications · doi:10.1038/s41467-018-03950-1 · Cited by 310 · see citing works
The annual cycles of phytoplankton biomass
Winder, M., & Cloern, J. E. (2010) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2010.0125 · Cited by 305 · see citing works
Mirelman, A., Maidan, I., Bernad‐Elazari, H., Nieuwhof, F., Reelick, M. F., Giladi, N., & Hausdorff, J. M. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-85 · Cited by 296 · see citing works
Does Gold Glitter in the Long-Run? Gold as a Hedge and Safe Haven Across Time and Investment Horizon
Bredın, D., Conlon, T., & Potì, V. (2014) · International Review of Financial Analysis · doi:10.1016/j.irfa.2015.01.010 · Cited by 297 · see citing works
Anchukaitis, K. J., Wilson, R. J. S., Briffa, K. R., Büntgen, U., Cook, E. R., D'Arrigo, R. D., Davi, N., Esper, J., Frank, D., Gunnarson, B. E., et al. (2017) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2017.02.020 · Cited by 294 · see citing works
Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster
Chan, A. H., Jenkins, P. A., & Song, Y. S. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1003090 · Cited by 292 · see citing works
Johansson, M. A., Cummings, D. A. T., & Glass, G. E. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000168 · Cited by 292 · see citing works
Ascoli, D., Vacchiano, G., Turco, M., Conedera, M., Drobyshev, I., Maringer, J., Motta, R., & Hacket‐Pain, A. J. (2017) · Nature Communications · doi:10.1038/s41467-017-02348-9 · Cited by 286 · see citing works
Nitrous oxide emissions are enhanced in a warmer and wetter world
Griffis, T. J., Chen, Z., Baker, J. M., Wood, J. D., Millet, D. B., Lee, X., Venterea, R. T., & Turner, P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1704552114 · Cited by 287 · see citing works
Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China
Zhang, Q., Xu, C., Jiang, T., & Wu, Y. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.08.010 · Cited by 283 · see citing works
Greenland Blocking Index 1851–2015: a regional climate change signal
Hanna, E., Cropper, T., Hall, R. J., & Cappelen, J. (2016) · International Journal of Climatology · doi:10.1002/joc.4673 · Cited by 264 · see citing works
Cheng, X., Li, X., & Hu, Y. (2015) · Human Brain Mapping · doi:10.1002/hbm.22754 · Cited by 258 · see citing works
Li, L., Qian, J., Ou, C., Zhou, Y.-X., Guo, C., & Guo, Y. (2014) · Environmental Pollution · doi:10.1016/j.envpol.2014.03.020 · Cited by 254 · see citing works
Diurnal thermocline oscillations driven by tidal flow around an island in the Middle Adriatic
Mihanović, H., Orlić, M., & Pasarić, Z. (2009) · Journal of Marine Systems · doi:10.1016/j.jmarsys.2009.01.021 · Cited by 254 · see citing works
Chien, F., Sadiq, M., Kamran, H. W., Nawaz, M. A., Hussain, M. S., & Raza, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-12938-2 · Cited by 250 · see citing works
Su, C., Khan, K. B., Tao, R., & Moldovan, N.-C. (2019) · Energy · doi:10.1016/j.energy.2019.116003 · Cited by 248 · see citing works
Sammer, G., Blecker, C. R., Gebhardt, H., Bischoff, M., Stark, R. M., Morgen, K., & Vaitl, D. (2006) · Human Brain Mapping · doi:10.1002/hbm.20309 · Cited by 245 · see citing works
ENSO and the South China Sea summer monsoon onset
Zhou, W., & Chan, J. C. L. (2006) · International Journal of Climatology · doi:10.1002/joc.1380 · Cited by 245 · see citing works
Nozawa, T., Sasaki, Y., Sakaki, K., Yokoyama, R., & Kawashima, R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.059 · Cited by 241 · see citing works
Diversifying equity with cryptocurrencies during COVID-19
Goodell, J. W., & Goutte, S. (2021) · International Review of Financial Analysis · doi:10.1016/j.irfa.2021.101781 · Cited by 241 · see citing works
Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis
Aloui, C., & Hkiri, B. (2013) · Economic Modelling · doi:10.1016/j.econmod.2013.09.043 · Cited by 239 · see citing works
McClymont, E. L., Sosdian, S., Rosell‐Melé, A., & Rosenthal, Y. (2013) · Earth-Science Reviews · doi:10.1016/j.earscirev.2013.04.006 · Cited by 233 · see citing works
Propagation dynamics from meteorological to groundwater drought and their possible influence factors
Han, Z., Huang, S., Huang, Q., Leng, G., Wang, H. E., Bai, Q., Zhao, J., Ma, L., Wang, L., & Du, M. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124102 · Cited by 232 · see citing works
Wang, F., Wang, Z., Yang, H., Di, D., Zhao, Y., Liang, Q., & Hussain, Z. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124751 · Cited by 232 · see citing works
The effects of interaction quality on neural synchrony during mother-child problem solving.
Nguyen, T., Schleihauf, H., Kayhan, E., Matthes, D., Vrtička, P., & Hoehl, S. (2019) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2019.11.020 · Cited by 208 · see citing works
Cross wavelet analyses of annual continental freshwater discharge and selected climate indices
Labat, D. (2010) · Journal of Hydrology · doi:10.1016/j.jhydrol.2010.02.029 · Cited by 228 · see citing works
Guo, L., van der Wegen, M., Jay, D. A., Matte, P., Wang, Z. B., Roelvink, D., & He, Q. (2015) · Journal of Geophysical Research Oceans · doi:10.1002/2014jc010491 · Cited by 225 · see citing works
Dynamics of large-scale electrophysiological networks: A technical review
O’Neill, G. C., Tewarie, P. K. B., Vidaurre, D., Liuzzi, L., Woolrich, M. W., & Brookes, M. J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.003 · Cited by 214 · see citing works
Hybrid Seismic Denoising Using Higher‐Order Statistics and Improved Wavelet Block Thresholding
Mousavi, S. M., & Langston, C. A. (2016) · Bulletin of the Seismological Society of America · doi:10.1785/0120150345 · Cited by 219 · see citing works
Extreme cyclone events in the Arctic: Wintertime variability and trends
Rinke, A., Maturilli, M., Graham, R. M., Matthes, H., Handorf, D., Cohen, L., Hudson, S. R., & Moore, J. C. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa7def · Cited by 220 · see citing works
Self-citationXu, M., Kang, S., Wu, H., & Yuan, X. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.12.007 · Cited by 220 · see citing works
Hu, W., & Si, B. (2016) · Hydrology and earth system sciences · doi:10.5194/hess-20-3183-2016 · Cited by 219 · see citing works
de Boer, B., Lourens, L. J., & van de Wal, R. S. W. (2014) · Nature Communications · doi:10.1038/ncomms3999 · Cited by 218 · see citing works
Iatsenko, D., McClintock, P. V. E., & Stefanovska, A. (2015) · Digital Signal Processing · doi:10.1016/j.dsp.2015.03.004 · Cited by 222 · see citing works
Links between eccentricity forcing and the 100,000-year glacial cycle
Lisiecki, L. E. (2010) · Nature Geoscience · doi:10.1038/ngeo828 · Cited by 216 · see citing works
Li, R., Chen, N., Zhang, X., Zeng, L., Wang, X., Tang, S., Li, D., & Niyogi, D. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.107809 · Cited by 215 · see citing works
Detailed assessment of climate variability in the Baltic Sea area for the period 1958 to 2009
Lehmann, A., Getzlaff, K., & Harlaß, J. (2010) · Climate Research · doi:10.3354/cr00876 · Cited by 214 · see citing works
Between-brain connectivity during imitation measured by fNIRS
Holper, L., Scholkmann, F., & Wolf, M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.028 · Cited by 211 · see citing works
Liu, N., Mok, C., Witt, E. E., Pradhan, A. H., Chen, J. E., & Reiss, A. L. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00082 · Cited by 208 · see citing works
Nonlinear rise in Greenland runoff in response to post-industrial Arctic warming
Trusel, L. D., Das, S. B., Osman, M., Evans, M. J., Smith, B. E., Fettweis, X., McConnell, J. R., Noel, B. P. Y., & van den Broeke, M. R. (2018) · Nature · doi:10.1038/s41586-018-0752-4 · Cited by 207 · see citing works
Yang, L., Cai, X. J., & Hamori, S. (2017) · International Review of Economics & Finance · doi:10.1016/j.iref.2017.03.015 · Cited by 206 · see citing works
Wu, L., Adebayo, T. S., Yue, G. X.-G., & Umut, A. (2022) · International Journal of Sustainable Development & World Ecology · doi:10.1080/13504509.2022.2115577 · Cited by 202 · see citing works
Tang, H., Mai, X., Wang, S., Zhu, C., Krueger, F., & Liu, C. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv092 · Cited by 198 · see citing works
Shen, M., Chen, J., Zhuan, M., Chen, H., Xu, C., & Xiong, L. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.11.004 · Cited by 198 · see citing works
Pan, Y., Novembre, G., Song, B., Li, X., & Hu, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.005 · Cited by 201 · see citing works
Cooperation makes two less-creative individuals turn into a highly-creative pair
Xue, H., Lu, K., & Hao, N. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.007 · Cited by 195 · see citing works
Ultralow frequency modulation of energetic particles in the dayside magnetosphere
Zong, Q., Zhou, X., Li, X., Song, P., Fu, S. Y., Baker, D. N., Pu, Z. Y., Fritz, T. A., Daly, P. W., Balogh, A., et al. (2007) · Geophysical Research Letters · doi:10.1029/2007gl029915 · Cited by 194 · see citing works
An entropy-based investigation into the variability of precipitation
Mishra, A. K., Özger, M., & Singh, V. P. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.03.006 · Cited by 192 · see citing works
Emile‐Geay, J., Cobb, K. M., MANN, M. E., & Wittenberg, A. T. (2012) · Journal of Climate · doi:10.1175/jcli-d-11-00511.1 · Cited by 195 · see citing works
Braun, U., Schäfer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E., Erk, S., Romanczuk‐Seiferth, N., Grimm, O., Geiger, L. S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608819113 · Cited by 183 · see citing works
Tiwari, A. K., Nasreen, S., Shahbaz, M. Q., & Hammoudeh, S. M. (2019) · Energy Economics · doi:10.1016/j.eneco.2019.104529 · Cited by 189 · see citing works
Zhou, Z., Shi, H., Fu, Q., Ding, Y., Li, T., Wang, Y., & Liu, S. (2021) · Journal of Geophysical Research Atmospheres · doi:10.1029/2020jd033959 · Cited by 189 · see citing works
Huang, S., Huang, Q., Chang, J., Leng, G., & Li, X. (2015) · Agricultural Water Management · doi:10.1016/j.agwat.2015.05.023 · Cited by 188 · see citing works
Maghyereh, A., Awartani, B. M. A., & Abdoh, H. (2018) · Energy · doi:10.1016/j.energy.2018.12.039 · Cited by 188 · see citing works
Pan, Y., Dikker, S., Goldstein, P., Zhu, Y., Yang, C., & Hu, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116657 · Cited by 187 · see citing works
Nalley, D., Adamowski, J., Biswas, A., Gharabaghi, B., & Hu, W. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.024 · Cited by 187 · see citing works
Cosford, J., Qing, H., Eglington, B. M., Mattey, D., Yuan, D., Zhang, M., & Cheng, H. (2008) · Earth and Planetary Science Letters · doi:10.1016/j.epsl.2008.08.018 · Cited by 186 · see citing works
Huang, L., Li, J., Hao, H., & Li, X. (2018) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2018.07.006 · Cited by 186 · see citing works
Guo, Y., Huang, S., Huang, Q., Wang, H. E., Fang, W., Yang, Y., & Wang, L. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.11.055 · Cited by 185 · see citing works
Iqbal, N., Fareed, Z., Shahzad, F., He, X., Shahzad, U., & Ma, L. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138916 · Cited by 184 · see citing works
Brain-to-brain synchronization across two persons predicts mutual prosociality
Hu, Y., Hu, Y., Li, X., Pan, Y., & Cheng, X. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx118 · Cited by 183 · see citing works
Li, Q., He, P., He, Y., Han, X., Zeng, T., Lu, G., & Wang, H. (2019) · Atmospheric Research · doi:10.1016/j.atmosres.2019.104743 · Cited by 184 · see citing works
Wavelet analysis in neurodynamics
Павлов, А. Н., Hramov, A. E., Koronovskii, A. A., Sitnikova, E. Y., Makarov, V. A., & Ovchinnikov, A. A. (2012) · Uspekhi Fizicheskih Nauk · doi:10.3367/ufnr.0182.201209a.0905 · Cited by 183 · see citing works
Stock market co-movements: Islamic versus conventional equity indices with multi-timescales analysis
Dewandaru, G., Rizvi, S. A. R., Masih, R., Masih, M., & Alhabshi, S. O. (2014) · Economic Systems · doi:10.1016/j.ecosys.2014.05.003 · Cited by 181 · see citing works
Enhancement of teaching outcome through neural prediction of the students' knowledge state
Zheng, L., Chen, C., Liu, W., Long, Y., Zhao, H., Bai, X., Zhang, Z., Han, Z., Liu, L., Guo, T., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24059 · Cited by 180 · see citing works
Novello, V. F., Cruz, F. W., Ivo Karmann, B., Burns, S. J., Stríkis, N. M., Vuille, M., Cheng, H., Edwards, R. L., Santos, R. V., Frigo, É., et al. (2012) · Geophysical Research Letters · doi:10.1029/2012gl053936 · Cited by 180 · see citing works
- Authors
- Grinsted, A., Moore, J. C., & Jevrejeva, S.
- Affiliations
- University of Lapland (FI)
- Venue
- Nonlinear Processes in Geophysics
- Publisher
- Copernicus GmbH
- Published
- 2004-11-18
- Volume
- 11
- Issue
- 5/6
- Pages
- 561-566
- ISSN
- 1023-5809 (ISSN-L)
- 1607-7946
- OpenAlex
- W2124459709
- Cited by
- 6,285
- References
- 15
- Citations by year
- 2026: 468 · 2025: 580 · 2024: 517 · 2023: 494 · 2022: 551 · 2021: 547 · 2020: 468 · 2019: 417 · 2018: 373 · 2017: 313 · 2016: 323 · 2015: 229 · 2014: 201 · 2013: 177 · 2012: 156
- FWCI
- 29.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nonlinear processes in geophysics (publishedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- LaCRIS (University of Lapland) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical Analysis and Transform Methods
- Keywords
- cross-wavelet transform, wavelet coherence, geophysical time series, time-frequency analysis, Monte Carlo significance testing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review