scholarly journals Análise de dados climáticos usando transformada em ondeletas cruzada e coerência

2020 ◽  
Vol 13 (2) ◽  
pp. 641
Author(s):  
Roseilson Souza Vale ◽  
Raoni Aquino Santana ◽  
Cléo Queresma Dias Júnior
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Wavelet Coherence ◽  
Climate Data ◽  
Mathematical Relationship ◽  
Cross Wavelet Transform ◽  
Climatic Time Series ◽  
Wavelet Coherence Analysis ◽  
Temperature Time ◽  
Cross Wavelet

Este estudo mostra uma análise em transformada em ondeleta cruzada e coerência em ondeleta aplicada a duas séries temporais, sendo uma delas precipitação e a outra temperatura do ar. O objetivo deste estudo é mostrar que esta técnica é uma ferramenta poderosa na análise de séries temporais climáticas, para isso à aplicamos a duas séries com relação física muito conhecida na climatologia. Além da aplicação realizada, recorreu-se também a uma descrição matemática dos métodos. A técnica da transformada em ondeletas cruzada e coerência mostrou-se eficiente em capturar a relação matemática entre as séries de precipitação e temperatura do ar. Com este estudo esperamos difundir o uso desta técnica para fins de ensino e pesquisa em diversos sistemas geofísicos. Analysis of Climate Data Using Transformed Crosswave and Coherence A B S T R A C TThis study presents a cross wavelet transform and wavelet coherence analysis applied to a precipitation and an air temperature time series. The objective of this study is to demonstrate that this technique is a powerful tool in the analysis of climatic time series, and can be applied to two time series with very well-known physical relationships in terms of climatology. In addition to this application, a mathematical description of the methods was done. The cross-curves and coherence technique proved to be efficient in capturing the mathematical relationship between precipitation series and air temperature. With this study we hope to disseminate the use of this technique for teaching and research purposes in various geophysical systems.Keywords: Phase Angle, Wavelet Coherence, Cross wavelet, Precipitation, Temperature. 

scholarly journals Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Tarun Pant ◽  
Maheswaran Rathinasamy ◽  
Ankit Agarwal
Keyword(s):  
Wavelet Transform ◽  
Temporal Variability ◽  
Global Climate ◽  
Phase Relationship ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Coherence Analysis ◽  
Cross Wavelet Transform ◽  
Wavelet Coherence Analysis ◽  
Cross Wavelet

Abstract With the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis was also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.

scholarly journals Application of the cross wavelet transform and wavelet coherence to geophysical time series

2004 ◽  
Vol 11 (5/6) ◽  
pp. 561-566 ◽  
Author(s):  
A. Grinsted ◽  
J. C. Moore ◽  
S. Jevrejeva
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Statistical Significance ◽  
Wavelet Coherence ◽  
The Arctic ◽  
Arctic Oscillation Index ◽  
Time Frequency ◽  
Cross Wavelet Transform ◽  
The Cross ◽  
Cross Wavelet

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/).

scholarly journals Wavelet analysis of land subsidence time-series: Madrid Tertiary aquifer case study

2020 ◽  
Vol 382 ◽  
pp. 353-359
Author(s):  
Roberto Tomás ◽  
José Luis Pastor ◽  
Marta Béjar-Pizarro ◽  
Roberta Bonì ◽  
Pablo Ezquerro ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Land Subsidence ◽  
Wavelet Coherence ◽  
Interferometric Synthetic Aperture Radar ◽  
Frequency Space ◽  
Time Frequency ◽  
Cross Wavelet Transform ◽  
Cross Wavelet

Abstract. Interpretation of land subsidence time-series to understand the evolution of the phenomenon and the existing relationships between triggers and measured displacements is a great challenge. Continuous wavelet transform (CWT) is a powerful signal processing method mainly suitable for the analysis of individual nonstationary time-series. CWT expands time-series into the time-frequency space allowing identification of localized nonstationary periodicities. Complementarily, Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) methods allow the comparison of two time-series that may be expected to be related in order to identify regions in the time-frequency domain that exhibit large common cross-power and wavelet coherence, respectively, and therefore are evocative of causality. In this work we use CWT, XWT and WTC to analyze piezometric and InSAR (interferometric synthetic aperture radar) time-series from the Tertiary aquifer of Madrid (Spain) to illustrate their capabilities for interpreting land subsidence and piezometric time-series information.

scholarly journals Non-stationary temporal characterization of the temperature profile of a soil exposed to frost in south-eastern Canada

2008 ◽  
Vol 15 (3) ◽  
pp. 409-416 ◽  
Author(s):  
F. Anctil ◽  
A. Pratte ◽  
L. E. Parent ◽  
M. A. Bolinder
Keyword(s):  
Time Series ◽  
Soil Temperature ◽  
Temperature Profile ◽  
Air Temperature ◽  
Soil Depth ◽  
Time Lag ◽  
Power Spectra ◽  
Temperature Time Series ◽  
Soil Temperatures ◽  
Temperature Time

Abstract. The objective of this work was to compare time and frequency fluctuations of air and soil temperatures (2-, 5-, 10-, 20- and 50-cm below the soil surface) using the continuous wavelet transform, with a particular emphasis on the daily cycle. The analysis of wavelet power spectra and cross power spectra provided detailed non-stationary accounts with respect to frequencies (or periods) and to time of the structure of the data and also of the relationships that exist between time series. For this particular application to the temperature profile of a soil exposed to frost, both the air temperature and the 2-cm depth soil temperature time series exhibited a dominant power peak at 1-d periodicity, prominent from spring to autumn. This feature was gradually damped as it propagated deeper into the soil and was weak for the 20-cm depth. Influence of the incoming solar radiation was also revealed in the wavelet power spectra analysis by a weaker intensity of the 1-d peak. The principal divergence between air and soil temperatures, besides damping, occurred in winter from the latent heat release associated to the freezing of the soil water and the insulation effect of snowpack that cease the dependence of the soil temperature to the air temperature. Attenuation and phase-shifting of the 1-d periodicity could be quantified through scale-averaged power spectra and time-lag estimations. Air temperature variance was only partly transferred to the 2-cm soil temperature time series and much less so to the 20-cm soil depth.

scholarly journals Uncertainty evaluation of Climatol ' s adjustment algorithm applied to daily air temperature time series

2020 ◽  
Author(s):  
Oleg Skrynyk ◽  
Enric Aguilar ◽  
Jose Guijarro ◽  
Luc Yannick Andreas Randriamarolaza ◽  
Sergiy Bubin
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Temperature Time Series ◽  
Uncertainty Evaluation ◽  
Temperature Time

scholarly journals Modeling ambient air temperature time series using neural networks

1998 ◽  
Vol 103 (D16) ◽  
pp. 19509-19517 ◽  
Author(s):  
G. Mihalakakou ◽  
M. Santamouris ◽  
D. Asimakopoulos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Air Temperature ◽  
Ambient Air ◽  
Temperature Time Series ◽  
Ambient Air Temperature ◽  
Temperature Time

scholarly journals Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

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